{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,24]],"date-time":"2026-05-24T00:09:52Z","timestamp":1779581392268,"version":"3.53.1"},"reference-count":288,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Applied Soft Computing"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1016\/j.asoc.2026.115122","type":"journal-article","created":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T16:16:27Z","timestamp":1774714587000},"page":"115122","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Unfolding graph convolutional networks: Architectures, use cases, and trends"],"prefix":"10.1016","volume":"196","author":[{"given":"Nematollah","family":"Saeidi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3974-6403","authenticated-orcid":false,"given":"Vahid","family":"Hajipour","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siavash","family":"Hekmat","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bijan","family":"Shoushtarian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.asoc.2026.115122_bib1","series-title":"2021 s International Conference on Electronics and Sustainable Communication Systems (ICESC)","first-page":"1183","article-title":"Graph neural network (GNN) in image and video understanding using deep learning for computer vision applications","author":"Pradhyumna","year":"2021"},{"key":"10.1016\/j.asoc.2026.115122_bib2","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1561\/2200000096","article-title":"Graph neural networks for natural language processing: a survey","volume":"16","author":"Wu","year":"2023","journal-title":"Found. Trends Mach. Learn."},{"key":"10.1016\/j.asoc.2026.115122_bib3","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2022.117921","article-title":"Graph neural network for traffic forecasting: a survey","volume":"207","author":"Jiang","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.asoc.2026.115122_bib4","series-title":"IEEE INFOCOM 2020 - IEEE Conference on Computer Communications","first-page":"914","article-title":"Guardian: evaluating trust in online social networks with graph convolutional networks","author":"Lin","year":"2020"},{"key":"10.1016\/j.asoc.2026.115122_bib5","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1109\/MNET.123.2100773","article-title":"Graph neural networks for communication networks: context, use cases and opportunities","volume":"37","author":"Su\u00e1rez-Varela","year":"2023","journal-title":"IEEE Netw."},{"key":"10.1016\/j.asoc.2026.115122_bib6","doi-asserted-by":"crossref","DOI":"10.1088\/2632-2153\/abbf9a","article-title":"Graph neural networks in particle physics","volume":"2","author":"Shlomi","year":"2021","journal-title":"Mach. Learn. Sci. Technol."},{"key":"10.1016\/j.asoc.2026.115122_bib7","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1038\/s43246-022-00315-6","article-title":"Graph neural networks for materials science and chemistry","volume":"3","author":"Reiser","year":"2022","journal-title":"Commun. Mater."},{"key":"10.1016\/j.asoc.2026.115122_bib8","doi-asserted-by":"crossref","first-page":"4758","DOI":"10.3390\/s21144758","article-title":"Graph-based deep learning for medical diagnosis and analysis: past, present and future","volume":"21","author":"Ahmedt-Aristizabal","year":"2021","journal-title":"Sensors"},{"key":"10.1016\/j.asoc.2026.115122_bib9","first-page":"1","article-title":"Graph neural networks in recommender systems: a survey","volume":"55","author":"Wu","year":"2023","journal-title":"ACM Comput. Surv."},{"key":"10.1016\/j.asoc.2026.115122_bib10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3543853","article-title":"A comprehensive survey on electronic design automation and graph neural networks: theory and applications","volume":"28","author":"S\u00e1nchez","year":"2023","journal-title":"ACM Trans. Des. Autom. Electron Syst."},{"key":"10.1016\/j.asoc.2026.115122_bib11","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1145\/3447556.3447566","article-title":"Adversarial attacks and defenses on graphs","volume":"22","author":"Jin","year":"2021","journal-title":"SIGKDD Explor Newsl."},{"key":"10.1016\/j.asoc.2026.115122_bib12","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-025-96945-0","article-title":"Comparative analysis of automated foul detection in football using deep learning architectures","volume":"15","author":"Rabee","year":"2025","journal-title":"Sci. Rep."},{"key":"10.1016\/j.asoc.2026.115122_bib13","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-025-09101-z","article-title":"ResNet-based image processing approach for precise detection of cracks in photovoltaic panels","volume":"15","author":"Abdelsattar","year":"2025","journal-title":"Sci. Rep."},{"key":"10.1016\/j.asoc.2026.115122_bib14","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-025-98607-7","article-title":"Comparative analysis of machine learning techniques for temperature and humidity prediction in photovoltaic environments","volume":"15","author":"Abdelsattar","year":"2025","journal-title":"Sci. Rep."},{"key":"10.1016\/j.asoc.2026.115122_bib15","unstructured":"T.N. Kipf, M. WellingSemi-Supervised Classification with Graph Convolutional Networks 2017. https:\/\/doi.org\/10.48550\/arXiv.1609.02907."},{"key":"10.1016\/j.asoc.2026.115122_bib16","article-title":"Convolutional neural networks on graphs with fast localized spectral filtering","volume":"29","author":"Defferrard","year":"2016"},{"key":"10.1016\/j.asoc.2026.115122_bib17","doi-asserted-by":"crossref","first-page":"41215","DOI":"10.1109\/ACCESS.2022.3166938","article-title":"Adaptive aggregation-transformation decoupled graph convolutional network for semi-supervised learning","volume":"10","author":"Sun","year":"2022","journal-title":"IEEE Access"},{"key":"10.1016\/j.asoc.2026.115122_bib18","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1186\/s40649-019-0069-y","article-title":"Graph convolutional networks: a comprehensive review","volume":"6","author":"Zhang","year":"2019","journal-title":"Comput. Soc. Netw."},{"key":"10.1016\/j.asoc.2026.115122_bib19","doi-asserted-by":"crossref","first-page":"70001","DOI":"10.1109\/ACCESS.2024.3398367","article-title":"A recommendation method based on multi-source heterogeneous hypergraphs and contrastive learning","volume":"12","author":"Wan","year":"2024","journal-title":"IEEE Access"},{"key":"10.1016\/j.asoc.2026.115122_bib20","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1109\/TKDE.2020.2981333","article-title":"Deep learning on graphs: a survey","volume":"34","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.asoc.2026.115122_bib21","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.aiopen.2021.01.001","article-title":"Graph neural networks: a review of methods and applications","volume":"1","author":"Zhou","year":"2020","journal-title":"AI Open"},{"key":"10.1016\/j.asoc.2026.115122_bib22","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/TNNLS.2020.2978386","article-title":"A comprehensive survey on graph neural networks","volume":"32","author":"Wu","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.asoc.2026.115122_bib23","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1109\/TAI.2021.3076021","article-title":"Graph learning: a survey","volume":"2","author":"Xia","year":"2021","journal-title":"IEEE Trans. Artif. Intell."},{"key":"10.1016\/j.asoc.2026.115122_bib24","first-page":"1","article-title":"Graph neural networks: taxonomy, advances, and trends","volume":"13","author":"Zhou","year":"2022","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"10.1016\/j.asoc.2026.115122_bib25","doi-asserted-by":"crossref","first-page":"6295","DOI":"10.1007\/s10462-022-10321-2","article-title":"A survey of graph neural networks in various learning paradigms: methods, applications, and challenges","volume":"56","author":"Waikhom","year":"2023","journal-title":"Artif. Intell. Rev."},{"key":"10.1016\/j.asoc.2026.115122_bib26","doi-asserted-by":"crossref","DOI":"10.1155\/2023\/8342104","article-title":"Deep learning with graph convolutional networks: an overview and latest applications in computational intelligence","volume":"2023","author":"Bhatti","year":"2023","journal-title":"Int. J. Intell. Syst."},{"key":"10.1016\/j.asoc.2026.115122_bib27","unstructured":"Wu F., Souza A., Zhang T., Fifty C., Yu T., Weinberger K. Simplifying graph convolutional networks. In: Proceedings of the 36th International Conference on Machine Learning, PMLR; 2019, p. 6861\u201371."},{"key":"10.1016\/j.asoc.2026.115122_bib28","unstructured":"H. Zhu, P. KoniuszSimple Spectral Graph Convolution. In: Proceedings of the International Conference on Learning Representations, 2020.."},{"key":"10.1016\/j.asoc.2026.115122_bib29","article-title":"LanczosNet: multi-scale deep graph convolutional networks","author":"Liao","year":"2018","journal-title":"Int. Conf. Learn. Represent."},{"key":"10.1016\/j.asoc.2026.115122_bib30","series-title":"2021 IEEE\/CVF International Conference on Computer Vision Workshops (ICCVW)","first-page":"2064","article-title":"Learning Laplacians in Chebyshev graph convolutional networks","author":"Sahbi","year":"2021"},{"key":"10.1016\/j.asoc.2026.115122_bib31","article-title":"Adaptive graph convolutional neural networks","volume":"32","author":"Li","year":"2018","journal-title":"AAAI"},{"key":"10.1016\/j.asoc.2026.115122_bib32","series-title":"Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW \u201918","first-page":"499","article-title":"Dual graph convolutional networks for graph-based semi-supervised classification","author":"Zhuang","year":"2018"},{"key":"10.1016\/j.asoc.2026.115122_bib33","doi-asserted-by":"crossref","first-page":"484","DOI":"10.1016\/j.ins.2019.11.019","article-title":"HesGCN: Hessian graph convolutional networks for semi-supervised classification","volume":"514","author":"Fu","year":"2020","journal-title":"Inf. Sci."},{"key":"10.1016\/j.asoc.2026.115122_bib34","doi-asserted-by":"crossref","first-page":"8004","DOI":"10.1609\/aaai.v35i9.16976","article-title":"Power up! Robust graph convolutional network via graph powering","volume":"35","author":"Jin","year":"2021","journal-title":"AAAI"},{"key":"10.1016\/j.asoc.2026.115122_bib35","unstructured":"Chen M., Wei Z., Huang Z., Ding B., Li Y. Simple and deep graph convolutional networks. In: Proceedings of the 37th International Conference on Machine Learning, PMLR; 2020, p. 1725\u201335."},{"key":"10.1016\/j.asoc.2026.115122_bib36","doi-asserted-by":"crossref","first-page":"105634","DOI":"10.1109\/ACCESS.2020.2999520","article-title":"Scalable graph convolutional networks with fast localized spectral filter for directed graphs","volume":"8","author":"Li","year":"2020","journal-title":"IEEE Access"},{"key":"10.1016\/j.asoc.2026.115122_bib37","unstructured":"Tam E., Dunson D. Fiedler regularization: learning neural networks with graph sparsity. In: Proceedings of the 37th International Conference on Machine Learning, PMLR; 2020, p. 9346\u201355."},{"key":"10.1016\/j.asoc.2026.115122_bib38","doi-asserted-by":"crossref","first-page":"2645","DOI":"10.1007\/s11063-020-10404-7","article-title":"Semi-supervised classification of graph convolutional networks with laplacian rank constraints","volume":"54","author":"Zhang","year":"2022","journal-title":"Neural Process Lett."},{"key":"10.1016\/j.asoc.2026.115122_bib39","article-title":"Graph wavelet neural network","author":"Xu","year":"2018","journal-title":"Int. Conf. Learn. Represent."},{"key":"10.1016\/j.asoc.2026.115122_bib40","doi-asserted-by":"crossref","first-page":"1259","DOI":"10.1093\/cercor\/bhaa292","article-title":"DS-GCNs: connectome classification using dynamic spectral graph convolution networks with assistant task training","volume":"31","author":"Xing","year":"2021","journal-title":"Cereb. Cortex"},{"key":"10.1016\/j.asoc.2026.115122_bib41","unstructured":"Tiao L.C., Elinas P., Nguyen H., Bonilla E.V. Variational spectral graph convolutional networks. ArXiv 2019."},{"key":"10.1016\/j.asoc.2026.115122_bib42","first-page":"17766","article-title":"Spectral temporal graph neural network for multivariate time-series forecasting","volume":"33","author":"Cao","year":"2020"},{"key":"10.1016\/j.asoc.2026.115122_bib43","unstructured":"Y. Chen, Y.R. Gel, K. AvrachenkovFractional graph convolutional networks (FGCN) for semi-supervised learning 2019.."},{"key":"10.1016\/j.asoc.2026.115122_bib44","unstructured":"Gilmer J., Schoenholz S.S., Riley P.F., Vinyals O., Dahl G.E. Neural message passing for quantum chemistry. In: Proceedings of the 34th International Conference on Machine Learning, PMLR; 2017, p. 1263\u201372."},{"key":"10.1016\/j.asoc.2026.115122_bib45","unstructured":"K. Xu, W. Hu, J. Leskovec, S. JegelkaHow powerful are graph neural networks? In: Proceedings of the International Conference on Learning Representations, 2018.."},{"key":"10.1016\/j.asoc.2026.115122_bib46","article-title":"Strategies for pre-training graph neural networks","author":"Hu","year":"2019","journal-title":"Int. Conf. Learn. Represent."},{"key":"10.1016\/j.asoc.2026.115122_bib47","article-title":"Inductive representation learning on large graphs","volume":"30","author":"Hamilton","year":"2017"},{"key":"10.1016\/j.asoc.2026.115122_bib48","series-title":"Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","first-page":"1416","article-title":"Large-scale learnable graph convolutional networks","author":"Gao","year":"2018"},{"key":"10.1016\/j.asoc.2026.115122_bib49","doi-asserted-by":"crossref","first-page":"3950","DOI":"10.1609\/aaai.v35i5.16514","article-title":"Beyond Low-frequency Information in Graph Convolutional Networks","volume":"35","author":"Bo","year":"2021","journal-title":"AAAI"},{"key":"10.1016\/j.asoc.2026.115122_bib50","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TPAMI.2021.3054830","article-title":"Graph neural networks with convolutional ARMA filters","author":"Bianchi","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.asoc.2026.115122_bib51","article-title":"Orbital graph convolutional neural network for material property prediction","volume":"4","author":"Karamad","year":"2020","journal-title":"Phys. Rev. Mater."},{"key":"10.1016\/j.asoc.2026.115122_bib52","unstructured":"Abu-El-Haija S., Perozzi B., Kapoor A., Alipourfard N., Lerman K., Harutyunyan H., et al. MixHop: Higher-order graph convolutional architectures via sparsified neighborhood mixing. In: Proceedings of the 36th International Conference on Machine Learning, PMLR; 2019, p. 21\u20139."},{"key":"10.1016\/j.asoc.2026.115122_bib53","doi-asserted-by":"crossref","first-page":"783","DOI":"10.1109\/TPAMI.2020.3011866","article-title":"Learning backtrackless aligned-spatial graph convolutional networks for graph classification","volume":"44","author":"Bai","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.asoc.2026.115122_bib54","doi-asserted-by":"crossref","first-page":"855","DOI":"10.26599\/TST.2021.9010066","article-title":"Graph convolutional network combined with semantic feature guidance for deep clustering","volume":"27","author":"Chen","year":"2022","journal-title":"Tsinghua Sci. Technol."},{"key":"10.1016\/j.asoc.2026.115122_bib55","doi-asserted-by":"crossref","unstructured":"J. Dai, Y. Wu, Z. Gao, Y. JiaA hyperbolic-to-hyperbolic graph convolutional network. In; Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 154\u2013163.","DOI":"10.1109\/CVPR46437.2021.00022"},{"key":"10.1016\/j.asoc.2026.115122_bib56","article-title":"Understanding and improving layer normalization","volume":"32","author":"Xu","year":"2019"},{"key":"10.1016\/j.asoc.2026.115122_bib57","unstructured":"Ioffe S., Szegedy C. Batch normalization: accelerating deep network training by reducing internal covariate shift. In: Proceedings of the 32nd International Conference on Machine Learning, PMLR; 2015, p. 448\u201356."},{"key":"10.1016\/j.asoc.2026.115122_bib58","unstructured":"Cai T., Luo S., Xu K., He D., Liu T.-Y., Wang L. GraphNorm: A Principled Approach to Accelerating Graph Neural Network Training. Proceedings of the 38th International Conference on Machine Learning, PMLR; 2021, p. 1204\u201315."},{"key":"10.1016\/j.asoc.2026.115122_bib59","author":"Dwivedi","year":"2022","journal-title":"Benchmark Graph Neural Netw."},{"key":"10.1016\/j.asoc.2026.115122_bib60","article-title":"Revisiting Over-smoothing","author":"Yang","year":"2020","journal-title":"Deep GCNs"},{"key":"10.1016\/j.asoc.2026.115122_bib61","article-title":"PairNorm: tackling oversmoothing in GNNs","author":"Zhao","year":"2019","journal-title":"Int. Conf. Learn. Represent."},{"key":"10.1016\/j.asoc.2026.115122_bib62","first-page":"4917","article-title":"Towards deeper graph neural networks with differentiable group normalization","volume":"33","author":"Zhou","year":"2020"},{"key":"10.1016\/j.asoc.2026.115122_bib63","first-page":"2220","article-title":"Rethinking pooling in graph neural networks","volume":"33","author":"Mesquita","year":"2020"},{"key":"10.1016\/j.asoc.2026.115122_bib64","article-title":"An end-to-end deep learning architecture for graph classification","volume":"32","author":"Zhang","year":"2018","journal-title":"AAAI"},{"key":"10.1016\/j.asoc.2026.115122_bib65","unstructured":"Diehl F. Edge contraction pooling for graph neural networks. CoRR 2019;abs\/1905.10990."},{"key":"10.1016\/j.asoc.2026.115122_bib66","unstructured":"Gao H., Ji S. Graph U-Nets. In: Proceedings of the 36th International Conference on Machine Learning, PMLR; 2019, p. 2083\u201392."},{"key":"10.1016\/j.asoc.2026.115122_bib67","author":"Cangea","year":"2018","journal-title":"Towards Sparse Hierarchical Graph Classif."},{"key":"10.1016\/j.asoc.2026.115122_bib68","article-title":"Understanding attention and generalization in graph neural networks","volume":"32","author":"Knyazev","year":"2019"},{"key":"10.1016\/j.asoc.2026.115122_bib69","unstructured":"Lee J., Lee I., Kang J. Self-attention graph pooling. In: Proceedings of the 36th International Conference on Machine Learning, PMLR; 2019, p. 3734\u201343."},{"key":"10.1016\/j.asoc.2026.115122_bib70","series-title":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","first-page":"723","article-title":"Graph convolutional networks with EigenPooling","author":"Ma","year":"2019"},{"key":"10.1016\/j.asoc.2026.115122_bib71","unstructured":"Bianchi F.M., Grattarola D., Alippi C. Spectral clustering with graph neural networks for graph pooling. In: Proceedings of the 37th International Conference on Machine Learning, PMLR; 2020, p. 874\u201383."},{"key":"10.1016\/j.asoc.2026.115122_bib72","doi-asserted-by":"crossref","first-page":"5470","DOI":"10.1609\/aaai.v34i04.5997","article-title":"ASAP: adaptive structure aware pooling for learning hierarchical graph representations","volume":"34","author":"Ranjan","year":"2020","journal-title":"AAAI"},{"key":"10.1016\/j.asoc.2026.115122_bib73","unstructured":"Khasahmadi A.H., Hassani K., Moradi P., Lee L., Morris Q. Memory-based graph networks 2020. \u3008https:\/\/doi.org\/10.48550\/arXiv.2002.09518\u3009."},{"key":"10.1016\/j.asoc.2026.115122_bib74","doi-asserted-by":"crossref","DOI":"10.1088\/1742-5468\/ac3ae4","article-title":"Path integral based convolution and pooling for graph neural networks","volume":"2021","author":"Ma","year":"2021","journal-title":"J. Stat. Mech."},{"key":"10.1016\/j.asoc.2026.115122_bib75","unstructured":"Z. Zhang, J. Bu, M. Ester, J. Zhang, C. Yao, Z. Yu, Hierarchical Graph Pooling with Structure Learning 2019. https:\/\/doi.org\/10.48550\/arXiv.1911.05954.."},{"key":"10.1016\/j.asoc.2026.115122_bib76","doi-asserted-by":"crossref","unstructured":"Liu M., Gao H., Ji S. Towards deeper graph neural networks. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Virtual Event CA USA: ACM; 2020, p. 338\u201348. \u3008https:\/\/doi.org\/10.1145\/3394486.3403076\u3009.","DOI":"10.1145\/3394486.3403076"},{"key":"10.1016\/j.asoc.2026.115122_bib77","doi-asserted-by":"crossref","DOI":"10.1016\/j.patter.2022.100491","article-title":"Scalable deeper graph neural networks for high-performance materials property prediction","volume":"3","author":"Omee","year":"2022","journal-title":"Patterns"},{"key":"10.1016\/j.asoc.2026.115122_bib78","doi-asserted-by":"crossref","first-page":"2769","DOI":"10.1109\/TPAMI.2022.3174515","article-title":"Bag of tricks for training deeper graph neural networks: a comprehensive benchmark study","volume":"45","author":"Chen","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.asoc.2026.115122_bib79","doi-asserted-by":"crossref","first-page":"7513","DOI":"10.1007\/s10489-021-02518-9","article-title":"GC-LSTM: graph convolution embedded LSTM for dynamic network link prediction","volume":"52","author":"Chen","year":"2022","journal-title":"Appl. Intell."},{"key":"10.1016\/j.asoc.2026.115122_bib80","series-title":"2019 53rd Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA","first-page":"467","article-title":"Fast graph convolutional recurrent neural networks","author":"Kadambari","year":"2019"},{"key":"10.1016\/j.asoc.2026.115122_bib81","doi-asserted-by":"crossref","first-page":"634","DOI":"10.2174\/1386207324666210215101825","article-title":"Predicting drug-target affinity based on recurrent neural networksand graph convolutional neural networks","volume":"25","author":"Tian","year":"2022","journal-title":"CCHTS"},{"key":"10.1016\/j.asoc.2026.115122_bib82","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1609\/aaai.v34i01.5393","article-title":"Rumor detection on social media with bi-directional graph convolutional networks","volume":"34","author":"Bian","year":"2020","journal-title":"AAAI"},{"key":"10.1016\/j.asoc.2026.115122_bib83","series-title":"2022 the 5th International Conference on Data Storage and Data Engineering (DSDE)","first-page":"85","article-title":"Short-term prediction model of water level based on ATT-ConvLSTM","author":"Liu","year":"2022"},{"key":"10.1016\/j.asoc.2026.115122_bib84","author":"Veli\u010dkovi\u0107","year":"2018","journal-title":"Graph Atten. Netw."},{"key":"10.1016\/j.asoc.2026.115122_bib85","series-title":"Proceedings of the 30th ACM International Conference on Information & Knowledge Management, Virtual Event Queensland","first-page":"2905","article-title":"Spectral graph attention network with fast eigen-approximation","author":"Chang","year":"2021"},{"key":"10.1016\/j.asoc.2026.115122_bib86","author":"Zhang","year":"2018","journal-title":"GaAN Gated Atten. Netw. Learn. Large Spatiotemporal Graphs"},{"key":"10.1016\/j.asoc.2026.115122_bib87","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbab174","article-title":"Multi-view multichannel attention graph convolutional network for miRNA\u2013disease association prediction","volume":"22","author":"Tang","year":"2021","journal-title":"Brief. Bioinforma."},{"key":"10.1016\/j.asoc.2026.115122_bib88","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbaa243","article-title":"Predicting drug\u2013disease associations through layer attention graph convolutional network","volume":"22","author":"Yu","year":"2021","journal-title":"Brief. Bioinforma."},{"key":"10.1016\/j.asoc.2026.115122_bib89","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2021.107868","article-title":"Multi-task learning for gait-based identity recognition and emotion recognition using attention enhanced temporal graph convolutional network","volume":"114","author":"Sheng","year":"2021","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.asoc.2026.115122_bib90","series-title":"2022 8th International Conference on Big Data and Information Analytics (BigDIA), Guiyang","first-page":"157","article-title":"GASN: a graph attention stochastic network against adversarial attack","author":"Wang","year":"2022"},{"key":"10.1016\/j.asoc.2026.115122_bib91","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2020.107798","article-title":"Image super-resolution via channel attention and spatial graph convolutional network","volume":"112","author":"Yang","year":"2021","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.asoc.2026.115122_bib92","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3363574","article-title":"Attention models in graphs: a survey","volume":"13","author":"Lee","year":"2019","journal-title":"ACM Trans. Knowl. Discov. Data"},{"key":"10.1016\/j.asoc.2026.115122_bib93","doi-asserted-by":"crossref","first-page":"2263","DOI":"10.1007\/s10462-023-10577-2","article-title":"Attention-based graph neural networks: a survey","volume":"56","author":"Sun","year":"2023","journal-title":"Artif. Intell. Rev."},{"key":"10.1016\/j.asoc.2026.115122_bib94","article-title":"D-VAE: a variational autoencoder for directed acyclic graphs","volume":"32","author":"Zhang","year":"2019"},{"key":"10.1016\/j.asoc.2026.115122_bib95","series-title":"2021 IEEE 15th International Symposium on Applied Computational Intelligence and Informatics (SACI)","first-page":"467","article-title":"Scalable graph convolutional variational autoencoders","author":"Unyi","year":"2021"},{"key":"10.1016\/j.asoc.2026.115122_bib96","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.neucom.2020.12.061","article-title":"AEGCN: an autoencoder-constrained graph convolutional network","volume":"432","author":"Ma","year":"2021","journal-title":"Neurocomputing"},{"key":"10.1016\/j.asoc.2026.115122_bib97","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2022.102375","article-title":"Disease prediction with edge-variational graph convolutional networks","volume":"77","author":"Huang","year":"2022","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.asoc.2026.115122_bib98","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.neucom.2022.02.002","article-title":"Degree aware based adversarial graph convolutional networks for entity alignment in heterogeneous knowledge graph","volume":"487","author":"Wang","year":"2022","journal-title":"Neurocomputing"},{"key":"10.1016\/j.asoc.2026.115122_bib99","series-title":"2021 Ninth International Symposium on Computing and Networking Workshops (CANDARW), Matsue","first-page":"173","article-title":"Adversarial attack detection on node classification by autoencoder-based analysis of hidden layers in graph convolutional networks","author":"Shimada","year":"2021"},{"key":"10.1016\/j.asoc.2026.115122_bib100","series-title":"2022 IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV)","first-page":"2753","article-title":"Generative adversarial graph convolutional networks for human action synthesis","author":"Degardin","year":"2022"},{"key":"10.1016\/j.asoc.2026.115122_bib101","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbac093","article-title":"IHGC-GAN: influence hypergraph convolutional generative adversarial network for risk prediction of late mild cognitive impairment based on imaging genetic data","volume":"23","author":"Bi","year":"2022","journal-title":"Brief. Bioinforma."},{"key":"10.1016\/j.asoc.2026.115122_bib102","series-title":"2019 International Joint Conference on Neural Networks (IJCNN)","first-page":"1","article-title":"GCGAN: generative adversarial nets with graph CNN for network-scale traffic prediction","author":"Zhang","year":"2019"},{"key":"10.1016\/j.asoc.2026.115122_bib103","unstructured":"Zhu Y., Du Y., Wang Y., Xu Y., Zhang J., Liu Q., et al. A survey on deep graph generation: methods and applications. In: Proceedings of the First Learning on Graphs Conference, PMLR; 2022, p. 47:1-47:21."},{"key":"10.1016\/j.asoc.2026.115122_bib104","doi-asserted-by":"crossref","first-page":"106675","DOI":"10.1109\/ACCESS.2021.3098417","article-title":"Deep graph generators: a survey","volume":"9","author":"Faez","year":"2021","journal-title":"IEEE Access"},{"key":"10.1016\/j.asoc.2026.115122_bib105","series-title":"ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","first-page":"3588","article-title":"Adaptive attention graph capsule network","author":"Zheng","year":"2022"},{"key":"10.1016\/j.asoc.2026.115122_bib106","series-title":"Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval","first-page":"1859","article-title":"Graph capsule network with a dual adaptive mechanism","author":"Zheng","year":"2022"},{"key":"10.1016\/j.asoc.2026.115122_bib107","author":"Verma","year":"2018","journal-title":"Graph Capsul. Convolutional Neural Netw."},{"key":"10.1016\/j.asoc.2026.115122_bib108","doi-asserted-by":"crossref","first-page":"1256","DOI":"10.1162\/neco_a_01493","article-title":"Graph clustering with graph capsule network","volume":"34","author":"Zhang","year":"2022","journal-title":"Neural Comput."},{"key":"10.1016\/j.asoc.2026.115122_bib109","doi-asserted-by":"crossref","first-page":"2903","DOI":"10.1049\/gtd2.12508","article-title":"GIS partial discharge pattern recognition via a novel capsule deep graph convolutional network","volume":"16","author":"Wang","year":"2022","journal-title":"IET Gener. Trans. Dist."},{"key":"10.1016\/j.asoc.2026.115122_bib110","doi-asserted-by":"crossref","first-page":"6991","DOI":"10.1109\/TNNLS.2022.3213589","article-title":"Geometric multimodal deep learning with multiscaled graph wavelet convolutional network","volume":"35","author":"Behmanesh","year":"2024","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.asoc.2026.115122_bib111","first-page":"595","article-title":"Multi-scale graph convolutional network with spectral graph wavelet frame","volume":"7","author":"Shen","year":"2021","journal-title":"IEEE Trans. Signal Inf. Process Netw."},{"key":"10.1016\/j.asoc.2026.115122_bib112","series-title":"2021 International Joint Conference on Neural Networks (IJCNN)","first-page":"1","article-title":"A deep graph wavelet convolutional neural network for semi-supervised node classification","author":"Wang","year":"2021"},{"key":"10.1016\/j.asoc.2026.115122_bib113","doi-asserted-by":"crossref","first-page":"4542","DOI":"10.1109\/TNNLS.2023.3285874","article-title":"Haar wavelet feature compression for quantized graph convolutional networks","volume":"35","author":"Eliasof","year":"2024","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.asoc.2026.115122_bib114","doi-asserted-by":"crossref","first-page":"5177","DOI":"10.3233\/JIFS-211729","article-title":"Dual graph wavelet neural network for graph-based semi-supervised classification","volume":"42","author":"Hu","year":"2022","journal-title":"IFS"},{"key":"10.1016\/j.asoc.2026.115122_bib115","doi-asserted-by":"crossref","first-page":"2898","DOI":"10.1080\/01431161.2020.1864056","article-title":"A pansharpening scheme using spectral graph wavelet transforms and convolutional neural networks","volume":"42","author":"Saxena","year":"2021","journal-title":"Int. J. Remote Sens."},{"key":"10.1016\/j.asoc.2026.115122_bib116","first-page":"5336","article-title":"Temporal neighbourhood aggregation: predicting future links in temporal graphs via recurrent variational graph convolutions","volume":"2019","author":"Bonner","year":"2019"},{"key":"10.1016\/j.asoc.2026.115122_bib117","unstructured":"Chen J., Pareja A., Domeniconi G., Ma T., Suzumura T., Kaler T., et al. Evolving graph convolutional networks for dynamic graphs. US11537852B2, 2022."},{"key":"10.1016\/j.asoc.2026.115122_bib118","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2019.107000","article-title":"Dynamic graph convolutional networks","volume":"97","author":"Manessi","year":"2020","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.asoc.2026.115122_bib119","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.neucom.2019.12.150","article-title":"Human activity recognition by manifold regularization based dynamic graph convolutional networks","volume":"444","author":"Liu","year":"2021","journal-title":"Neurocomputing"},{"key":"10.1016\/j.asoc.2026.115122_bib120","doi-asserted-by":"crossref","first-page":"4611","DOI":"10.1609\/aaai.v36i4.20385","article-title":"DDGCN: dual dynamic graph convolutional networks for rumor detection on social media","volume":"36","author":"Sun","year":"2022","journal-title":"AAAI"},{"key":"10.1016\/j.asoc.2026.115122_bib121","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.inffus.2021.07.013","article-title":"Interpretable learning based dynamic graph convolutional networks for Alzheimer\u2019s disease analysis","volume":"77","author":"Zhu","year":"2022","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.asoc.2026.115122_bib122","doi-asserted-by":"crossref","first-page":"7261","DOI":"10.1002\/int.22880","article-title":"Malware detection with dynamic evolving graph convolutional networks","volume":"37","author":"Zhang","year":"2022","journal-title":"Int J. Intell. Syst."},{"key":"10.1016\/j.asoc.2026.115122_bib123","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3483595","article-title":"A survey on embedding dynamic graphs","volume":"55","author":"Barros","year":"2023","journal-title":"ACM Comput. Surv."},{"key":"10.1016\/j.asoc.2026.115122_bib124","doi-asserted-by":"crossref","first-page":"79143","DOI":"10.1109\/ACCESS.2021.3082932","article-title":"Foundations and modeling of dynamic networks using dynamic graph neural networks: a survey","volume":"9","author":"Skarding","year":"2021","journal-title":"IEEE Access"},{"key":"10.1016\/j.asoc.2026.115122_bib125","doi-asserted-by":"crossref","first-page":"3182","DOI":"10.1109\/TMM.2021.3094296","article-title":"Self-supervised graph convolutional network for multi-view clustering","volume":"24","author":"Xia","year":"2022","journal-title":"IEEE Trans. Multimed."},{"key":"10.1016\/j.asoc.2026.115122_bib126","unstructured":"You Y., Chen T., Wang Z., Shen Y. When does self-supervision help graph convolutional networks? Proceedings of the 37th International Conference on Machine Learning, PMLR; 2020, p. 10871\u201380."},{"key":"10.1016\/j.asoc.2026.115122_bib127","doi-asserted-by":"crossref","first-page":"10049","DOI":"10.1609\/aaai.v35i11.17206","article-title":"Contrastive and generative graph convolutional networks for graph-based semi-supervised learning","volume":"35","author":"Wan","year":"2021","journal-title":"AAAI"},{"key":"10.1016\/j.asoc.2026.115122_bib128","doi-asserted-by":"crossref","first-page":"5892","DOI":"10.1609\/aaai.v34i04.6048","article-title":"Multi-stage self-supervised learning for graph convolutional networks on graphs with few labeled nodes","volume":"34","author":"Sun","year":"2020","journal-title":"AAAI"},{"key":"10.1016\/j.asoc.2026.115122_bib129","unstructured":"Kim D., Oh A. How to find your friendly neighborhood: graph attention design with self-supervision. In: Proceedings of the 9th International Conference on Learning Representations, ICLR 2021, 2021."},{"key":"10.1016\/j.asoc.2026.115122_bib130","article-title":"GraphCDR: a graph neural network method with contrastive learning for cancer drug response prediction","volume":"23","author":"Liu","year":"2022","journal-title":"Brief. Bioinforma."},{"key":"10.1016\/j.asoc.2026.115122_bib131","series-title":"Proceedings of the Web Conference 2021","first-page":"413","article-title":"Self-supervised multi-channel hypergraph convolutional network for social recommendation","author":"Yu","year":"2021"},{"key":"10.1016\/j.asoc.2026.115122_bib132","series-title":"Document Analysis Systems","first-page":"96","article-title":"Contrastive graph learning with graph convolutional networks","author":"Nagendar","year":"2022"},{"key":"10.1016\/j.asoc.2026.115122_bib133","doi-asserted-by":"crossref","first-page":"2412","DOI":"10.1109\/TPAMI.2022.3170559","article-title":"Self-supervised learning of graph neural networks: a unified review","volume":"45","author":"Xie","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.asoc.2026.115122_bib134","first-page":"1","article-title":"Graph self-supervised learning: a survey","author":"Liu","year":"2022","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.asoc.2026.115122_bib135","author":"Jiang","year":"2020","journal-title":"Graph Convolutional Reinf. Learn."},{"key":"10.1016\/j.asoc.2026.115122_bib136","series-title":"ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","first-page":"3510","article-title":"Graphcomm: a graph neural network based method for multi-agent reinforcement learning","author":"Shen","year":"2021"},{"key":"10.1016\/j.asoc.2026.115122_bib137","doi-asserted-by":"crossref","first-page":"703","DOI":"10.3390\/machines10080703","article-title":"Deep reinforcement learning based on social spatial\u2013temporal graph convolution network for crowd navigation","volume":"10","author":"Lu","year":"2022","journal-title":"Machines"},{"key":"10.1016\/j.asoc.2026.115122_bib138","doi-asserted-by":"crossref","DOI":"10.1016\/j.dsp.2022.103419","article-title":"A new ensemble deep graph reinforcement learning network for spatio-temporal traffic volume forecasting in a freeway network","volume":"123","author":"Shang","year":"2022","journal-title":"Digit. Signal Process."},{"key":"10.1016\/j.asoc.2026.115122_bib139","unstructured":"A.D. McNaughton, M.S. Bontha, C.R. Knutson, J.A. Pope, N. KumarDe novo design of protein target specific scaffold-based Inhibitors via Reinforcement Learning 2022. https:\/\/doi.org\/10.48550\/arXiv.2205.10473."},{"key":"10.1016\/j.asoc.2026.115122_bib140","doi-asserted-by":"crossref","first-page":"31823","DOI":"10.1109\/ACCESS.2020.2973140","article-title":"Deep reinforcement learning-based channel allocation for wireless LANs with graph convolutional networks","volume":"8","author":"Nakashima","year":"2020","journal-title":"IEEE Access"},{"key":"10.1016\/j.asoc.2026.115122_bib141","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1016\/j.ins.2021.07.007","article-title":"Dynamic graph convolutional network for long-term traffic flow prediction with reinforcement learning","volume":"578","author":"Peng","year":"2021","journal-title":"Inf. Sci."},{"key":"10.1016\/j.asoc.2026.115122_bib142","doi-asserted-by":"crossref","first-page":"23981","DOI":"10.1109\/JIOT.2022.3188826","article-title":"Joint routing and scheduling optimization in time-sensitive networks using graph-convolutional-network-based deep reinforcement learning","volume":"9","author":"Yang","year":"2022","journal-title":"IEEE Internet Things J."},{"key":"10.1016\/j.asoc.2026.115122_bib143","doi-asserted-by":"crossref","first-page":"15051","DOI":"10.1109\/TNNLS.2023.3283523","article-title":"Challenges and opportunities in deep reinforcement learning with graph neural networks: a comprehensive review of algorithms and applications","volume":"35","author":"Munikoti","year":"2024","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.asoc.2026.115122_bib144","doi-asserted-by":"crossref","first-page":"1065","DOI":"10.1109\/TETCI.2022.3222545","article-title":"Reinforcement learning on graphs: a survey","volume":"7","author":"Nie","year":"2023","journal-title":"IEEE Trans. Emerg. Top. Comput. Intell."},{"key":"10.1016\/j.asoc.2026.115122_bib145","doi-asserted-by":"crossref","unstructured":"Tian Y., Chen G., Song Y., Wan X. Dependency-driven Relation Extraction with Attentive Graph Convolutional Networks. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), Online: Association for Computational Linguistics; 2021, p. 4458\u201371. \u3008https:\/\/doi.org\/10.18653\/v1\/2021.acl-long.344\u3009.","DOI":"10.18653\/v1\/2021.acl-long.344"},{"key":"10.1016\/j.asoc.2026.115122_bib146","series-title":"Proceedings of the Web Conference 2021","first-page":"2547","article-title":"Pathfinder discovery networks for neural message passing","author":"Rozemberczki","year":"2021"},{"key":"10.1016\/j.asoc.2026.115122_bib147","doi-asserted-by":"crossref","first-page":"90","DOI":"10.3390\/jcp5040090","article-title":"AI-powered security for IoT ecosystems: a hybrid deep learning approach to anomaly detection","volume":"5","author":"Kumar","year":"2025","journal-title":"JCP"},{"key":"10.1016\/j.asoc.2026.115122_bib148","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.acha.2010.04.005","article-title":"Wavelets on graphs via spectral graph theory","volume":"30","author":"Hammond","year":"2011","journal-title":"Appl. Comput. Harmon. Anal."},{"key":"10.1016\/j.asoc.2026.115122_bib149","doi-asserted-by":"crossref","first-page":"4216","DOI":"10.1109\/TKDE.2021.3131584","article-title":"Self-supervised learning on graphs: contrastive, generative, or predictive","volume":"35","author":"Wu","year":"2023","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.asoc.2026.115122_bib150","author":"Zhu","year":"2020","journal-title":"Deep Graph Contrastive Represent. Learn."},{"key":"10.1016\/j.asoc.2026.115122_bib151","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2022.109631","article-title":"Graph barlow twins: a self-supervised representation learning framework for graphs","volume":"256","author":"Bielak","year":"2022","journal-title":"Knowl. -Based Syst."},{"key":"10.1016\/j.asoc.2026.115122_bib152","unstructured":"Hassani K., Khasahmadi A.H. Contrastive multi-view representation learning on graphs. In: Proceedings of the 37th International Conference on Machine Learning, PMLR; 2020, p. 4116\u201326."},{"key":"10.1016\/j.asoc.2026.115122_bib153","first-page":"93","article-title":"Collective classification in network data","volume":"29","author":"Sen","year":"2008","journal-title":"AI Mag."},{"key":"10.1016\/j.asoc.2026.115122_bib154","doi-asserted-by":"crossref","unstructured":"Giles C.L., Bollacker K.D., Lawrence S. CiteSeer: an automatic citation indexing system. Proceedings of the third ACM conference on Digital libraries - DL \u201998, Pittsburgh, Pennsylvania, United States: ACM Press; 1998, p. 89\u201398. \u3008https:\/\/doi.org\/10.1145\/276675.276685\u3009.","DOI":"10.1145\/276675.276685"},{"key":"10.1016\/j.asoc.2026.115122_bib155","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1023\/A:1009953814988","article-title":"Automating the construction of internet portals with machine learning","volume":"3","author":"McCallum","year":"2000","journal-title":"Inf. Retr."},{"key":"10.1016\/j.asoc.2026.115122_bib156","first-page":"22118","article-title":"Open graph benchmark: datasets for machine learning on graphs","volume":"33","author":"Hu","year":"2020"},{"key":"10.1016\/j.asoc.2026.115122_bib157","author":"Shchur","year":"2019","journal-title":"Pitfalls Graph Neural Netw. Eval."},{"key":"10.1016\/j.asoc.2026.115122_bib158","doi-asserted-by":"crossref","first-page":"i190","DOI":"10.1093\/bioinformatics\/btx252","article-title":"Predicting multicellular function through multi-layer tissue networks","volume":"33","author":"Zitnik","year":"2017","journal-title":"Bioinformatics"},{"key":"10.1016\/j.asoc.2026.115122_bib159","series-title":"Proceedings of the 29th International Coference on International Conference on Machine Learning","first-page":"291","article-title":"Subgraph matching kernels for attributed graphs","author":"Kriege","year":"2012"},{"key":"10.1016\/j.asoc.2026.115122_bib160","doi-asserted-by":"crossref","first-page":"786","DOI":"10.1021\/jm00106a046","article-title":"Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds. Correlation with molecular orbital energies and hydrophobicity","volume":"34","author":"Debnath","year":"1991","journal-title":"J. Med. Chem."},{"key":"10.1016\/j.asoc.2026.115122_bib161","doi-asserted-by":"crossref","first-page":"i47","DOI":"10.1093\/bioinformatics\/bti1007","article-title":"Protein function prediction via graph kernels","volume":"21","author":"Borgwardt","year":"2005","journal-title":"Bioinformatics"},{"key":"10.1016\/j.asoc.2026.115122_bib162","doi-asserted-by":"crossref","first-page":"771","DOI":"10.1016\/S0022-2836(03)00628-4","article-title":"Distinguishing enzyme structures from non-enzymes without alignments","volume":"330","author":"Dobson","year":"2003","journal-title":"J. Mol. Biol."},{"key":"10.1016\/j.asoc.2026.115122_bib163","series-title":"Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","first-page":"1365","article-title":"Deep graph kernels","author":"Yanardag","year":"2015"},{"key":"10.1016\/j.asoc.2026.115122_bib164","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1007\/s10115-007-0103-5","article-title":"Comparison of descriptor spaces for chemical compound retrieval and classification","volume":"14","author":"Wale","year":"2008","journal-title":"Knowl. Inf. Syst."},{"key":"10.1016\/j.asoc.2026.115122_bib165","article-title":"Weisfeiler-lehman graph kernels","volume":"12","author":"Shervashidze","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.asoc.2026.115122_bib166","series-title":"Proceedings of the 20th international conference on World wide web","first-page":"577","article-title":"Efficient k-nearest neighbor graph construction for generic similarity measures","author":"Dong","year":"2011"},{"key":"10.1016\/j.asoc.2026.115122_bib167","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1145\/1217299.1217301","article-title":"Graph evolution: densification and shrinking diameters","volume":"1","author":"Leskovec","year":"2007","journal-title":"ACM Trans. Knowl. Discov. Data"},{"key":"10.1016\/j.asoc.2026.115122_bib168","series-title":"2016 IEEE 16th International Conference on Data Mining (ICDM)","first-page":"221","article-title":"Edge weight prediction in weighted signed networks","author":"Kumar","year":"2016"},{"key":"10.1016\/j.asoc.2026.115122_bib169","series-title":"Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining","first-page":"990","article-title":"ArnetMiner: extraction and mining of academic social networks","author":"Tang","year":"2008"},{"key":"10.1016\/j.asoc.2026.115122_bib170","series-title":"The World Wide Web Conference","first-page":"2022","article-title":"Heterogeneous graph attention network","author":"Wang","year":"2019"},{"key":"10.1016\/j.asoc.2026.115122_bib171","series-title":"Proceedings of The Web Conference 2020","first-page":"2704","article-title":"Heterogeneous graph transformer","author":"Hu","year":"2020"},{"key":"10.1016\/j.asoc.2026.115122_bib172","article-title":"GraphSAINT: graph sampling based inductive learning method","author":"Zeng","year":"2019","journal-title":"Int. Conf. Learn. Represent."},{"key":"10.1016\/j.asoc.2026.115122_bib173","series-title":"Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)","article-title":"OGB-LSC: a large-scale challenge for machine learning on graphs","author":"Hu","year":"2021"},{"key":"10.1016\/j.asoc.2026.115122_bib174","series-title":"Proceedings of the 3rd Workshop on Continuous Vector Space Models and their Compositionality","first-page":"57","article-title":"Observed versus latent features for knowledge base and text inference","author":"Toutanova","year":"2015"},{"key":"10.1016\/j.asoc.2026.115122_bib175","doi-asserted-by":"crossref","DOI":"10.1609\/aaai.v32i1.11573","article-title":"Convolutional 2D knowledge graph embeddings","volume":"32","author":"Dettmers","year":"2018","journal-title":"AAAI"},{"key":"10.1016\/j.asoc.2026.115122_bib176","series-title":"IEEE INFOCOM 2022 - IEEE Conference on Computer Communications","first-page":"1029","article-title":"AoI-minimal UAV crowdsensing by model-based graph convolutional reinforcement learning","author":"Dai","year":"2022"},{"key":"10.1016\/j.asoc.2026.115122_bib177","doi-asserted-by":"crossref","first-page":"29163","DOI":"10.1007\/s11042-021-11130-5","article-title":"Graph convolutional network-based reinforcement learning for tasks offloading in multi-access edge computing","volume":"80","author":"Leng","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"10.1016\/j.asoc.2026.115122_bib178","doi-asserted-by":"crossref","first-page":"5216","DOI":"10.3390\/s20185216","article-title":"A graph convolutional network-based deep reinforcement learning approach for resource allocation in a cognitive radio network","volume":"20","author":"Zhao","year":"2020","journal-title":"Sensors"},{"key":"10.1016\/j.asoc.2026.115122_bib179","doi-asserted-by":"crossref","first-page":"1564","DOI":"10.1109\/LCOMM.2020.3048995","article-title":"Discovering attack scenarios via intrusion alert correlation using graph convolutional networks","volume":"25","author":"Cheng","year":"2021","journal-title":"IEEE Commun. Lett."},{"key":"10.1016\/j.asoc.2026.115122_bib180","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.comcom.2021.12.015","article-title":"Graph-based deep learning for communication networks: a survey","volume":"185","author":"Jiang","year":"2022","journal-title":"Comput. Commun."},{"key":"10.1016\/j.asoc.2026.115122_bib181","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3565973","article-title":"Graph neural networks in IoT: a survey","volume":"19","author":"Dong","year":"2023","journal-title":"ACM Trans. Sen. Netw."},{"key":"10.1016\/j.asoc.2026.115122_bib182","doi-asserted-by":"crossref","DOI":"10.1155\/2021\/1690669","article-title":"GCNRDM: a social network rumor detection method based on graph convolutional network in mobile computing","volume":"2021","author":"Xu","year":"2021","journal-title":"Wirel. Commun. Mob. Comput."},{"key":"10.1016\/j.asoc.2026.115122_bib183","doi-asserted-by":"crossref","first-page":"4774","DOI":"10.1007\/s10489-020-02036-0","article-title":"Detection of rumor conversations in Twitter using graph convolutional networks","volume":"51","author":"Lotfi","year":"2021","journal-title":"Appl. Intell."},{"key":"10.1016\/j.asoc.2026.115122_bib184","doi-asserted-by":"crossref","first-page":"1054","DOI":"10.1609\/aaai.v34i01.5455","article-title":"Graph convolutional networks with markov random field reasoning for social spammer detection","volume":"34","author":"Wu","year":"2020","journal-title":"AAAI"},{"key":"10.1016\/j.asoc.2026.115122_bib185","article-title":"Graph convolutional networks fusing motif-structure information","volume":"12","author":"Wang","year":"2022","journal-title":"Sci. Rep."},{"key":"10.1016\/j.asoc.2026.115122_bib186","first-page":"1","article-title":"FACS-GCN: fairness-aware cost-sensitive boosting of graph convolutional networks","volume":"2022","author":"Santos","year":"2022"},{"key":"10.1016\/j.asoc.2026.115122_bib187","first-page":"1","article-title":"Unsupervised community detection algorithm based on graph convolution network and social media","volume":"2022","author":"Zhou","year":"2022","journal-title":"Mob. Inf. Syst."},{"key":"10.1016\/j.asoc.2026.115122_bib188","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.neucom.2021.05.058","article-title":"Unsupervised learning for community detection in attributed networks based on graph convolutional network","volume":"456","author":"Wang","year":"2021","journal-title":"Neurocomputing"},{"key":"10.1016\/j.asoc.2026.115122_bib189","series-title":"2022 8th International Conference on Advanced Computing and Communication Systems (ICACCS)","first-page":"1204","article-title":"An important sampling over graph convolutional network for community detection","author":"Meena","year":"2022"},{"key":"10.1016\/j.asoc.2026.115122_bib190","first-page":"3","article-title":"Using relational graph convolutional networks to assign fashion communities to users","volume":"830","author":"Budhiraja","year":"2022"},{"key":"10.1016\/j.asoc.2026.115122_bib191","doi-asserted-by":"crossref","first-page":"2838","DOI":"10.1609\/aaai.v34i03.5673","article-title":"Going deep: graph convolutional ladder-shape networks","volume":"34","author":"Hu","year":"2020","journal-title":"AAAI"},{"key":"10.1016\/j.asoc.2026.115122_bib192","series-title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, Yokohama, Japan","first-page":"2879","article-title":"Multi-class imbalanced graph convolutional network learning","author":"Shi","year":"2020"},{"key":"10.1016\/j.asoc.2026.115122_bib193","doi-asserted-by":"crossref","first-page":"1342","DOI":"10.1038\/s41592-021-01255-8","article-title":"SpaGCN: integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network","volume":"18","author":"Hu","year":"2021","journal-title":"Nat. Methods"},{"key":"10.1016\/j.asoc.2026.115122_bib194","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2022.106069","article-title":"MHDMF: prediction of miRNA\u2013disease associations based on deep matrix factorization with multi-source graph convolutional network","volume":"149","author":"Ai","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.asoc.2026.115122_bib195","doi-asserted-by":"crossref","first-page":"4210","DOI":"10.1609\/aaai.v36i4.20340","article-title":"Powerful graph convolutional networks with adaptive propagation mechanism for homophily and heterophily","volume":"36","author":"Wang","year":"2022","journal-title":"AAAI"},{"key":"10.1016\/j.asoc.2026.115122_bib196","article-title":"Graph neural networks and their current applications in bioinformatics","volume":"12","author":"Zhang","year":"2021","journal-title":"Front. Genet."},{"key":"10.1016\/j.asoc.2026.115122_bib197","doi-asserted-by":"crossref","first-page":"799","DOI":"10.1093\/bioinformatics\/btab729","article-title":"HPODNets: deep graph convolutional networks for predicting human protein\u2013phenotype associations","volume":"38","author":"Liu","year":"2022","journal-title":"Bioinformatics"},{"key":"10.1016\/j.asoc.2026.115122_bib198","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1093\/bioinformatics\/btab643","article-title":"Structure-aware protein\u2013protein interaction site prediction using deep graph convolutional network","volume":"38","author":"Yuan","year":"2021","journal-title":"Bioinformatics"},{"key":"10.1016\/j.asoc.2026.115122_bib199","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbab319","article-title":"Drug repositioning based on the heterogeneous information fusion graph convolutional network","volume":"22","author":"Cai","year":"2021","journal-title":"Brief. Bioinforma."},{"key":"10.1016\/j.asoc.2026.115122_bib200","doi-asserted-by":"crossref","DOI":"10.1016\/j.ab.2022.114631","article-title":"deepMDDI: a deep graph convolutional network framework for multi-label prediction of drug-drug interactions","volume":"646","author":"Feng","year":"2022","journal-title":"Anal. Biochem."},{"key":"10.1016\/j.asoc.2026.115122_bib201","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1109\/TCBB.2020.2999084","article-title":"Graph convolutional autoencoder and generative adversarial network-based method for predicting drug-target interactions","volume":"19","author":"Sun","year":"2022","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinf."},{"key":"10.1016\/j.asoc.2026.115122_bib202","unstructured":"Sanchez-Gonzalez A., Heess N., Springenberg J.T., Merel J., Riedmiller M., Hadsell R., et al. Graph networks as learnable physics engines for inference and control. In: Proceedings of the 35th International Conference on Machine Learning, PMLR; 2018, p. 4470\u20139."},{"key":"10.1016\/j.asoc.2026.115122_bib203","doi-asserted-by":"crossref","DOI":"10.1088\/0256-307X\/39\/6\/067503","article-title":"Self-supervised graph neural networks for accurate prediction of N\u00e9el temperature","volume":"39","author":"Kong","year":"2022","journal-title":"Chin. Phys. Lett."},{"key":"10.1016\/j.asoc.2026.115122_bib204","doi-asserted-by":"crossref","DOI":"10.21468\/SciPostPhys.12.1.045","article-title":"Deep set auto encoders for anomaly detection in particle physics","volume":"12","author":"Ostdiek","year":"2022","journal-title":"SciPost Phys."},{"key":"10.1016\/j.asoc.2026.115122_bib205","article-title":"Physics-aware difference graph networks for sparsely-observed dynamics","author":"Seo","year":"2019","journal-title":"Int. Conf. Learn. Represent."},{"key":"10.1016\/j.asoc.2026.115122_bib206","unstructured":"Alet F., Jeewajee A.K., Villalonga M.B., Rodriguez A., Lozano-Perez T., Kaelbling L. Graph Element Networks: adaptive, structured computation and memory. In: Proceedings of the 36th International Conference on Machine Learning, PMLR; 2019, p. 212\u201322."},{"key":"10.1016\/j.asoc.2026.115122_bib207","first-page":"P10004","article-title":"Partition pooling for convolutional graph network applications in particle physics","volume":"17","author":"Bachlechner","year":"2022","journal-title":"J. Inst."},{"key":"10.1016\/j.asoc.2026.115122_bib208","doi-asserted-by":"crossref","DOI":"10.1016\/j.trc.2021.103466","article-title":"DDP-GCN: Multi-graph convolutional network for spatiotemporal traffic forecasting","volume":"134","author":"Lee","year":"2022","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"10.1016\/j.asoc.2026.115122_bib209","doi-asserted-by":"crossref","first-page":"35973","DOI":"10.1109\/ACCESS.2021.3062114","article-title":"AST-GCN: attribute-augmented spatiotemporal graph convolutional network for traffic forecasting","volume":"9","author":"Zhu","year":"2021","journal-title":"IEEE Access"},{"key":"10.1016\/j.asoc.2026.115122_bib210","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2021.115992","article-title":"Spatiotemporal multi-graph convolutional networks with synthetic data for traffic volume forecasting","volume":"187","author":"Zhu","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.asoc.2026.115122_bib211","doi-asserted-by":"crossref","first-page":"2348","DOI":"10.1109\/TKDE.2020.3008774","article-title":"Predicting citywide crowd flows in irregular regions using multi-view graph convolutional networks","volume":"34","author":"Sun","year":"2022","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.asoc.2026.115122_bib212","doi-asserted-by":"crossref","first-page":"303","DOI":"10.3390\/rs14020303","article-title":"Region-level traffic prediction based on temporal multi-spatial dependence graph convolutional network from GPS data","volume":"14","author":"Yang","year":"2022","journal-title":"Remote Sens."},{"key":"10.1016\/j.asoc.2026.115122_bib213","first-page":"1","article-title":"Make more connections: urban traffic flow forecasting with spatiotemporal adaptive gated graph convolution network","volume":"13","author":"Lu","year":"2022","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"10.1016\/j.asoc.2026.115122_bib214","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.neucom.2020.09.043","article-title":"Deep spatio-temporal graph convolutional network for traffic accident prediction","volume":"423","author":"Yu","year":"2021","journal-title":"Neurocomputing"},{"key":"10.1016\/j.asoc.2026.115122_bib215","series-title":"CICTP 2021","first-page":"467","article-title":"Traffic Prediction with Graph Neural Network: A Survey","author":"Liu","year":"2021"},{"key":"10.1016\/j.asoc.2026.115122_bib216","doi-asserted-by":"crossref","first-page":"6459","DOI":"10.1109\/ACCESS.2022.3142922","article-title":"Confidence-based simple graph convolutional networks for face clustering","volume":"10","author":"Sun","year":"2022","journal-title":"IEEE Access"},{"key":"10.1016\/j.asoc.2026.115122_bib217","series-title":"Proceedings of the Thirteenth Language Resources and Evaluation Conference","doi-asserted-by":"crossref","first-page":"7328","DOI":"10.63317\/2pkthubupe7n","article-title":"Bidirectional skeleton-based isolated sign recognition using graph convolutional networks","author":"Dafnis","year":"2022"},{"key":"10.1016\/j.asoc.2026.115122_bib218","first-page":"1","article-title":"EMS-GCN: an end-to-end mixhop superpixel-based graph convolutional network for hyperspectral image classification","volume":"60","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.asoc.2026.115122_bib219","doi-asserted-by":"crossref","first-page":"5751","DOI":"10.1109\/TNNLS.2021.3071369","article-title":"Deep feature aggregation framework driven by graph convolutional network for scene classification in remote sensing","volume":"33","author":"Xu","year":"2022","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.asoc.2026.115122_bib220","series-title":"2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)","first-page":"3457","article-title":"Isolated sign language recognition with multi-scale spatial-temporal graph convolutional networks","author":"Vazquez-Enriquez","year":"2021"},{"key":"10.1016\/j.asoc.2026.115122_bib221","doi-asserted-by":"crossref","first-page":"2009","DOI":"10.1080\/13658816.2022.2048834","article-title":"Application of a graph convolutional network with visual and semantic features to classify urban scenes","volume":"36","author":"Xu","year":"2022","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"10.1016\/j.asoc.2026.115122_bib222","doi-asserted-by":"crossref","first-page":"10297","DOI":"10.1109\/TPAMI.2024.3445463","article-title":"A survey on graph neural networks and graph transformers in computer vision: a task-oriented perspective","volume":"46","author":"Chen","year":"2024","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.asoc.2026.115122_bib223","doi-asserted-by":"crossref","first-page":"13387","DOI":"10.1007\/s00521-022-07368-1","article-title":"Applications of graph convolutional networks in computer vision","volume":"34","author":"Cao","year":"2022","journal-title":"Neural Comput. & Applic"},{"key":"10.1016\/j.asoc.2026.115122_bib224","series-title":"2022 International Joint Conference on Neural Networks (IJCNN)","first-page":"1","article-title":"Graph neural networks in computer vision - architectures, datasets and common approaches","author":"Krzywda","year":"2022"},{"key":"10.1016\/j.asoc.2026.115122_bib225","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2022.109250","article-title":"Graph convolutional networks in language and vision: a survey","volume":"251","author":"Ren","year":"2022","journal-title":"Knowl. -Based Syst."},{"key":"10.1016\/j.asoc.2026.115122_bib226","doi-asserted-by":"crossref","first-page":"1819","DOI":"10.1109\/TMM.2022.3168137","article-title":"Joint-bone fusion graph convolutional network for semi-supervised skeleton action recognition","volume":"25","author":"Tu","year":"2023","journal-title":"IEEE Trans. Multimed."},{"key":"10.1016\/j.asoc.2026.115122_bib227","doi-asserted-by":"crossref","first-page":"2317","DOI":"10.1007\/s10489-021-02487-z","article-title":"Predictively encoded graph convolutional network for noise-robust skeleton-based action recognition","volume":"52","author":"Yoon","year":"2022","journal-title":"Appl. Intell."},{"key":"10.1016\/j.asoc.2026.115122_bib228","series-title":"2021 IEEE\/CVF International Conference on Computer Vision (ICCV)","first-page":"11447","article-title":"MSR-GCN: multi-scale residual graph convolution networks for human motion prediction","author":"Dang","year":"2021"},{"key":"10.1016\/j.asoc.2026.115122_bib229","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2021.108503","article-title":"Identifying players in broadcast videos using graph convolutional network","volume":"124","author":"Feng","year":"2022","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.asoc.2026.115122_bib230","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2021.108170","article-title":"Action recognition via pose-based graph convolutional networks with intermediate dense supervision","volume":"121","author":"Shi","year":"2022","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.asoc.2026.115122_bib231","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2022.104675","article-title":"Temporal segment graph convolutional networks for skeleton-based action recognition","volume":"110","author":"Ding","year":"2022","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.asoc.2026.115122_bib232","doi-asserted-by":"crossref","DOI":"10.1016\/j.jbi.2022.104000","article-title":"MedGCN: Medication recommendation and lab test imputation via graph convolutional networks","volume":"127","author":"Mao","year":"2022","journal-title":"J. Biomed. Inform."},{"key":"10.1016\/j.asoc.2026.115122_bib233","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.ins.2021.10.040","article-title":"Food recommendation with graph convolutional network","volume":"584","author":"Gao","year":"2022","journal-title":"Inf. Sci."},{"key":"10.1016\/j.asoc.2026.115122_bib234","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.neunet.2021.11.010","article-title":"Multi-layer information fusion based on graph convolutional network for knowledge-driven herb recommendation","volume":"146","author":"Yang","year":"2022","journal-title":"Neural Netw."},{"key":"10.1016\/j.asoc.2026.115122_bib235","series-title":"2022 IEEE 38th International Conference on Data Engineering (ICDE)","first-page":"299","article-title":"Attentive knowledge-aware graph convolutional networks with collaborative guidance for personalized recommendation","author":"Chen","year":"2022"},{"key":"10.1016\/j.asoc.2026.115122_bib236","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2022.108628","article-title":"Personalized knowledge-aware recommendation with collaborative and attentive graph convolutional networks","volume":"128","author":"Dai","year":"2022","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.asoc.2026.115122_bib237","doi-asserted-by":"crossref","first-page":"595","DOI":"10.1016\/j.ins.2022.01.001","article-title":"SocialLGN: light graph convolution network for social recommendation","volume":"589","author":"Liao","year":"2022","journal-title":"Inf. Sci."},{"key":"10.1016\/j.asoc.2026.115122_bib238","doi-asserted-by":"crossref","first-page":"9192","DOI":"10.1002\/int.22986","article-title":"Incorporating multi-interest into recommendation with graph convolution networks","volume":"37","author":"Jiang","year":"2022","journal-title":"Int J. Intell. Sys"},{"key":"10.1016\/j.asoc.2026.115122_bib239","article-title":"Inductive matrix completion based on graph neural networks","volume":"2020","author":"Zhang","year":"2020"},{"key":"10.1016\/j.asoc.2026.115122_bib240","doi-asserted-by":"crossref","first-page":"5045","DOI":"10.1609\/aaai.v34i04.5945","article-title":"Memory augmented graph neural networks for sequential recommendation","volume":"34","author":"Ma","year":"2020","journal-title":"AAAI"},{"key":"10.1016\/j.asoc.2026.115122_bib241","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1609\/aaai.v34i01.5330","article-title":"Revisiting graph based collaborative filtering: a linear residual graph convolutional network approach","volume":"34","author":"Chen","year":"2020","journal-title":"AAAI"},{"key":"10.1016\/j.asoc.2026.115122_bib242","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3661821","article-title":"A survey of graph neural networks for social recommender systems","volume":"56","author":"Sharma","year":"2024","journal-title":"ACM Comput. Surv."},{"key":"10.1016\/j.asoc.2026.115122_bib243","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2021.115712","article-title":"GL-GCN: global and local dependency guided graph convolutional networks for aspect-based sentiment classification","volume":"186","author":"Zhu","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.asoc.2026.115122_bib244","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2021.107643","article-title":"Aspect-based sentiment analysis via affective knowledge enhanced graph convolutional networks","volume":"235","author":"Liang","year":"2022","journal-title":"Knowl. -Based Syst."},{"key":"10.1016\/j.asoc.2026.115122_bib245","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1007\/s41060-022-00315-2","article-title":"Learning from word semantics to sentence syntax by graph convolutional networks for aspect-based sentiment analysis","volume":"14","author":"Dai","year":"2022","journal-title":"Int. J. Data Sci. Anal."},{"key":"10.1016\/j.asoc.2026.115122_bib246","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2021.107080","article-title":"Syntax-type-aware graph convolutional networks for natural language understanding","volume":"102","author":"Du","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.asoc.2026.115122_bib247","series-title":"2021 IEEE International Conference on Data Mining (ICDM)","first-page":"837","article-title":"AS-GCN: adaptive semantic architecture of graph convolutional networks for text-rich networks","author":"Yu","year":"2021"},{"key":"10.1016\/j.asoc.2026.115122_bib248","series-title":"2022 IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV)","first-page":"2533","article-title":"Post-OCR paragraph recognition by graph convolutional networks","author":"Wang","year":"2022"},{"key":"10.1016\/j.asoc.2026.115122_bib249","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3451215","article-title":"Knowledge-aware multi-modal adaptive graph convolutional networks for fake news detection","volume":"17","author":"Qian","year":"2021","journal-title":"ACM Trans. Multimed. Comput. Commun. Appl."},{"key":"10.1016\/j.asoc.2026.115122_bib250","first-page":"2974","article-title":"Enhanced multi-channel graph convolutional network for aspect sentiment triplet extraction","volume":"1","author":"Chen","year":"2022"},{"key":"10.1016\/j.asoc.2026.115122_bib251","series-title":"2021 IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","first-page":"0084","article-title":"Review of graph neural network in text classification","author":"Malekzadeh","year":"2021"},{"key":"10.1016\/j.asoc.2026.115122_bib252","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijepes.2020.106753","article-title":"Data-driven short-term voltage stability assessment based on spatial-temporal graph convolutional network","volume":"130","author":"Luo","year":"2021","journal-title":"Int. J. Electr. Power & Energy Syst."},{"key":"10.1016\/j.asoc.2026.115122_bib253","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijepes.2020.106647","article-title":"A short-term voltage stability online prediction method based on graph convolutional networks and long short-term memory networks","volume":"127","author":"Wang","year":"2021","journal-title":"Int. J. Electr. Power & Energy Syst."},{"key":"10.1016\/j.asoc.2026.115122_bib254","doi-asserted-by":"crossref","DOI":"10.1088\/1742-6596\/2095\/1\/012011","article-title":"Power system small-signal stability assessment model based on residual graph convolutional networks","volume":"2095","author":"Su","year":"2021","journal-title":"J. Phys Conf. Ser."},{"key":"10.1016\/j.asoc.2026.115122_bib255","doi-asserted-by":"crossref","first-page":"1119","DOI":"10.1007\/s11265-021-01724-5","article-title":"AIGCN: attack intention detection for power system using graph convolutional networks","volume":"94","author":"Tang","year":"2022","journal-title":"J. Sign Process Syst."},{"key":"10.1016\/j.asoc.2026.115122_bib256","series-title":"2021 2nd International Conference on Secure Cyber Computing and Communications (ICSCCC","first-page":"279","article-title":"Android malware detection using function call graph with graph convolutional networks","author":"VK","year":"2021"},{"key":"10.1016\/j.asoc.2026.115122_bib257","doi-asserted-by":"crossref","DOI":"10.1016\/j.cose.2021.102264","article-title":"GDroid: Android malware detection and classification with graph convolutional network","volume":"106","author":"Gao","year":"2021","journal-title":"Comput. & Secur."},{"key":"10.1016\/j.asoc.2026.115122_bib258","series-title":"2022 32nd International Conference on Field-Programmable Logic and Applications (FPL), Belfast, United Kingdom","first-page":"200","article-title":"H-GCN: a graph convolutional network accelerator on versal ACAP architecture","author":"Zhang","year":"2022"},{"key":"10.1016\/j.asoc.2026.115122_bib259","doi-asserted-by":"crossref","first-page":"9492","DOI":"10.3390\/s22239492","article-title":"A survey on graph neural networks for microservice-based cloud applications","volume":"22","author":"Nguyen","year":"2022","journal-title":"Sensors"},{"key":"10.1016\/j.asoc.2026.115122_bib260","doi-asserted-by":"crossref","first-page":"3321","DOI":"10.1007\/s11063-022-10764-2","article-title":"A targeted universal attack on graph convolutional network by using fake nodes","volume":"54","author":"Dai","year":"2022","journal-title":"Neural Process Lett."},{"key":"10.1016\/j.asoc.2026.115122_bib261","series-title":"Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Virtual Event CA","first-page":"1656","article-title":"Certifiable robustness of graph convolutional networks under structure perturbations","author":"Z\u00fcgner","year":"2020"},{"key":"10.1016\/j.asoc.2026.115122_bib262","series-title":"Proceedings of the 14th ACM International Conference on Web Search and Data Mining, Virtual Event","first-page":"148","article-title":"Node similarity preserving graph convolutional networks","author":"Jin","year":"2021"},{"key":"10.1016\/j.asoc.2026.115122_bib263","series-title":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Anchorage AK","first-page":"1399","article-title":"Robust graph convolutional networks against adversarial attacks","author":"Zhu","year":"2019"},{"key":"10.1016\/j.asoc.2026.115122_bib264","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2021.108255","article-title":"SARS-net: COVID-19 detection from chest x-rays by combining graph convolutional network and convolutional neural network","volume":"122","author":"Kumar","year":"2022","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.asoc.2026.115122_bib265","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1016\/j.inffus.2020.10.004","article-title":"Covid-19 classification by FGCNet with deep feature fusion from graph convolutional network and convolutional neural network","volume":"67","author":"Wang","year":"2021","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.asoc.2026.115122_bib266","doi-asserted-by":"crossref","DOI":"10.1016\/j.compmedimag.2021.102027","article-title":"A survey on graph-based deep learning for computational histopathology","volume":"95","author":"Ahmedt-Aristizabal","year":"2022","journal-title":"Comput. Med. Imaging Graph."},{"key":"10.1016\/j.asoc.2026.115122_bib267","doi-asserted-by":"crossref","first-page":"41361","DOI":"10.1007\/s11042-020-09634-7","article-title":"A comprehensive survey on convolutional neural network in medical image analysis","volume":"81","author":"Yao","year":"2022","journal-title":"Multimed. Tools Appl."},{"key":"10.1016\/j.asoc.2026.115122_bib268","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1006376","article-title":"Alzheimer\u2019s disease neuroimaging initiative. modeling and prediction of clinical symptom trajectories in alzheimer\u2019s disease using longitudinal data","volume":"14","author":"Bhagwat","year":"2018","journal-title":"PLOS Comput. Biol."},{"key":"10.1016\/j.asoc.2026.115122_bib269","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1038\/s42256-021-00325-y","article-title":"Integration of multiomics data with graph convolutional networks to identify new cancer genes and their associated molecular mechanisms","volume":"3","author":"Schulte-Sasse","year":"2021","journal-title":"Nat. Mach. Intell."},{"key":"10.1016\/j.asoc.2026.115122_bib270","doi-asserted-by":"crossref","DOI":"10.1016\/j.ipm.2020.102439","article-title":"Improved breast cancer classification through combining graph convolutional network and convolutional neural network","volume":"58","author":"Zhang","year":"2021","journal-title":"Inf. Process. & Manag."},{"key":"10.1016\/j.asoc.2026.115122_bib271","doi-asserted-by":"crossref","first-page":"1279","DOI":"10.1109\/TMI.2021.3051604","article-title":"A mutual multi-scale triplet graph convolutional network for classification of brain disorders using functional or structural connectivity","volume":"40","author":"Yao","year":"2021","journal-title":"IEEE Trans. Med Imaging"},{"key":"10.1016\/j.asoc.2026.115122_bib272","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbab432","article-title":"Improving cancer driver gene identification using multi-task learning on graph convolutional network","volume":"23","author":"Peng","year":"2022","journal-title":"Brief. Bioinforma."},{"key":"10.1016\/j.asoc.2026.115122_bib273","author":"Saeidi","year":"2024","journal-title":"Leverag-.-. Med. Found. Model Features Graph Neural Netw. -Based Retr. Breast Histopathol. Images"},{"key":"10.1016\/j.asoc.2026.115122_bib274","doi-asserted-by":"crossref","first-page":"135423","DOI":"10.1109\/ACCESS.2021.3114691","article-title":"Traffic message channel prediction based on graph convolutional network","volume":"9","author":"Li","year":"2021","journal-title":"IEEE Access"},{"key":"10.1016\/j.asoc.2026.115122_bib275","series-title":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","first-page":"4840","article-title":"Graph neural networks: foundation, frontiers and applications","author":"Wu","year":"2022"},{"key":"10.1016\/j.asoc.2026.115122_bib276","doi-asserted-by":"crossref","first-page":"824","DOI":"10.1109\/TPAMI.2018.2889473","article-title":"Efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs","volume":"42","author":"Malkov","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.asoc.2026.115122_bib277","series-title":"2017 Fourth International Conference on Signal Processing, Communication and Networking (ICSCN)","first-page":"1","article-title":"FLANN: Fast approximate nearest neighbour search algorithm for elucidating human-wildlife conflicts in forest areas","author":"Suju","year":"2017"},{"key":"10.1016\/j.asoc.2026.115122_bib278","unstructured":"Guo R., Sun P., Lindgren E., Geng Q., Simcha D., Chern F., et al. Accelerating large-scale inference with anisotropic vector quantization. Proceedings of the 37th International Conference on Machine Learning, PMLR; 2020, p. 3887\u201396."},{"key":"10.1016\/j.asoc.2026.115122_bib279","doi-asserted-by":"crossref","DOI":"10.1016\/j.is.2019.02.006","article-title":"ANN-benchmarks: a benchmarking tool for approximate nearest neighbor algorithms","volume":"87","author":"Aum\u00fcller","year":"2020","journal-title":"Inf. Syst."},{"key":"10.1016\/j.asoc.2026.115122_bib280","series-title":"Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Virtual Event CA","first-page":"1857","article-title":"GPT-GNN: generative pre-training of graph neural networks","author":"Hu","year":"2020"},{"key":"10.1016\/j.asoc.2026.115122_bib281","doi-asserted-by":"crossref","first-page":"4276","DOI":"10.1609\/aaai.v35i5.16552","article-title":"Learning to pre-train graph neural networks","volume":"35","author":"Lu","year":"2021","journal-title":"AAAI"},{"key":"10.1016\/j.asoc.2026.115122_bib282","series-title":"Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Virtual Event CA","first-page":"1150","article-title":"GCC: Graph contrastive coding for graph neural network pre-training","author":"Qiu","year":"2020"},{"key":"10.1016\/j.asoc.2026.115122_bib283","unstructured":"Zhao Q., Ren W., Li T., Liu H., He X., Xu X. GraphGPT: generative pre-trained graph eulerian transformer. In: Proceedings of the 42nd International Conference on Machine Learning, PMLR; 2025, p. 77630\u201356."},{"key":"10.1016\/j.asoc.2026.115122_bib284","article-title":"A large-scale training paradigm for graph generative models","author":"Wang","year":"2024","journal-title":"Thirteen. Int. Conf. Learn. Represent."},{"key":"10.1016\/j.asoc.2026.115122_bib285","doi-asserted-by":"crossref","first-page":"26083","DOI":"10.1609\/aaai.v39i24.34804","article-title":"BindGPT: a scalable framework for 3D molecular design via language modeling and reinforcement learning","volume":"39","author":"Zholus","year":"2025","journal-title":"AAAI"},{"key":"10.1016\/j.asoc.2026.115122_bib286","article-title":"Unified generative modeling of 3D molecules with Bayesian flow networks","author":"Song","year":"2023","journal-title":"Twelfth Int. Conf. Learn. Represent."},{"key":"10.1016\/j.asoc.2026.115122_bib287","unstructured":"G. Li, C. Xiong, A. Thabet, B. Ghanem, 2020, DeeperGCN: all you need to train deeper GCNs 2020. https:\/\/doi.org\/10.48550\/arXiv.2006.07739.."},{"key":"10.1016\/j.asoc.2026.115122_bib288","article-title":"Hierarchical graph representation learning with differentiable pooling","volume":"31","author":"Ying","year":"2018"}],"container-title":["Applied Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1568494626005703?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1568494626005703?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,23]],"date-time":"2026-05-23T23:37:14Z","timestamp":1779579434000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1568494626005703"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":288,"alternative-id":["S1568494626005703"],"URL":"https:\/\/doi.org\/10.1016\/j.asoc.2026.115122","relation":{},"ISSN":["1568-4946"],"issn-type":[{"value":"1568-4946","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Unfolding graph convolutional networks: Architectures, use cases, and trends","name":"articletitle","label":"Article Title"},{"value":"Applied Soft Computing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.asoc.2026.115122","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"115122"}}