{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T09:12:22Z","timestamp":1769937142667,"version":"3.49.0"},"reference-count":60,"publisher":"Springer Science and Business Media LLC","issue":"16","license":[{"start":{"date-parts":[[2024,7,8]],"date-time":"2024-07-08T00:00:00Z","timestamp":1720396800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,7,8]],"date-time":"2024-07-08T00:00:00Z","timestamp":1720396800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"published-print":{"date-parts":[[2024,11]]},"DOI":"10.1007\/s11227-024-06336-x","type":"journal-article","created":{"date-parts":[[2024,7,8]],"date-time":"2024-07-08T06:02:00Z","timestamp":1720418520000},"page":"24638-24663","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Centrality-based and similarity-based neighborhood extension in graph neural networks"],"prefix":"10.1007","volume":"80","author":[{"given":"Mohammadjavad","family":"Zohrabi","sequence":"first","affiliation":[]},{"given":"Saeed","family":"Saravani","sequence":"additional","affiliation":[]},{"given":"Mostafa","family":"Haghir Chehreghani","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,7,8]]},"reference":[{"key":"6336_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.mlwa.2022.100401","volume":"10","author":"A Keramatfar","year":"2022","unstructured":"Keramatfar A, Rafiee M, Amirkhani H (2022) Graph neural networks: a bibliometrics overview. Mach Learn Appl 10:100401. https:\/\/doi.org\/10.1016\/j.mlwa.2022.100401","journal-title":"Mach Learn Appl"},{"key":"6336_CR2","doi-asserted-by":"publisher","unstructured":"Wu Y, Lian D, Xu Y, Wu L, Chen E (2020) Graph convolutional networks with markov random field reasoning for social spammer detection. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol 34, pp 1054\u20131061. https:\/\/doi.org\/10.48550\/arXiv.2005.11079","DOI":"10.48550\/arXiv.2005.11079"},{"key":"6336_CR3","doi-asserted-by":"publisher","first-page":"35973","DOI":"10.1109\/ACCESS.2021.3062114","volume":"9","author":"J Zhu","year":"2021","unstructured":"Zhu J, Wang Q, Tao C, Deng H, Zhao L, Li H (2021) Ast-gcn: attribute-augmented spatiotemporal graph convolutional network for traffic forecasting. IEEE Access 9:35973\u201335983. https:\/\/doi.org\/10.1109\/ACCESS.2021.3062114","journal-title":"IEEE Access"},{"key":"6336_CR4","doi-asserted-by":"publisher","DOI":"10.5555\/3295222.3295399","author":"A Fout","year":"2017","unstructured":"Fout A, Byrd J, Shariat B, Ben-Hur A (2017) Protein interface prediction using graph convolutional networks. Adv Neural Inf Process Syst. https:\/\/doi.org\/10.5555\/3295222.3295399","journal-title":"Adv Neural Inf Process Syst"},{"key":"6336_CR5","doi-asserted-by":"publisher","unstructured":"Gholinejad N, Chehreghani MH (2024) Heterophily-aware fair recommendation using graph convolutional networks. In: CoRR https:\/\/doi.org\/10.48550\/ARXIV.2402.03365arxiv: 2402.03365","DOI":"10.48550\/ARXIV.2402.03365"},{"issue":"3","key":"6336_CR6","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1007\/S10462-024-10702-9","volume":"57","author":"B Lakzaei","year":"2024","unstructured":"Lakzaei B, Chehreghani MH, Bagheri A (2024) Disinformation detection using graph neural networks: a survey. Artif Intell Rev 57(3):52. https:\/\/doi.org\/10.1007\/S10462-024-10702-9","journal-title":"Artif Intell Rev"},{"key":"6336_CR7","doi-asserted-by":"publisher","unstructured":"Peng H, Li J, He Y, Liu Y, Bao M, Wang L, Song Y, Yang Q (2018) Large-scale hierarchical text classification with recursively regularized deep graph-cnn. In: Proceedings of the 2018 World Wide Web Conference, pp 1063\u20131072 https:\/\/doi.org\/10.1145\/3178876.3186005","DOI":"10.1145\/3178876.3186005"},{"key":"6336_CR8","doi-asserted-by":"publisher","unstructured":"Zhang M, Cui Z, Neumann M, Chen Y (2018) An end-to-end deep learning architecture for graph classification. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol 32 https:\/\/doi.org\/10.1609\/aaai.v32i1.11782","DOI":"10.1609\/aaai.v32i1.11782"},{"key":"6336_CR9","doi-asserted-by":"publisher","unstructured":"Veli\u010dkovi\u0107 P, Cucurull G, Casanova A, Romero A, Lio P, Bengio Y (2018) Graph attention networks. In: International Conference on Learning Representations (ICLR) https:\/\/doi.org\/10.48550\/arXiv.1710.10903","DOI":"10.48550\/arXiv.1710.10903"},{"key":"6336_CR10","unstructured":"Hasan MA, Chaoji V, Salem S, Zaki M (2006) Link prediction using supervised learning. In: SDM06: Workshop on Link Analysis, Counter-terrorism and Security, pp 798\u2013805"},{"key":"6336_CR11","doi-asserted-by":"publisher","unstructured":"Bianchi FM, Grattarola D, Alippi C (2020) Spectral clustering with graph neural networks for graph pooling. In: International Conference on Machine Learning, PMLR, pp 874\u2013883 https:\/\/doi.org\/10.48550\/arXiv.1907.00481","DOI":"10.48550\/arXiv.1907.00481"},{"key":"6336_CR12","doi-asserted-by":"publisher","unstructured":"Ghanbari M, Chehreghani MH (2022) Graph clustering using node embeddings: an empirical study. In: IEEE International Conference on Big Data, Big Data 2022. IEEE, Osaka, Japan, pp 5488\u20135493. https:\/\/doi.org\/10.1109\/BIGDATA55660.2022.10020377","DOI":"10.1109\/BIGDATA55660.2022.10020377"},{"key":"6336_CR13","doi-asserted-by":"publisher","unstructured":"Xu K, Hu W, Leskovec J, Jegelka S (2018) How powerful are graph neural networks? arXiv preprint https:\/\/doi.org\/10.48550\/arXiv.1810.00826arXiv:1810.00826","DOI":"10.48550\/arXiv.1810.00826"},{"key":"6336_CR14","doi-asserted-by":"publisher","unstructured":"Gutteridge B, Dong X, Bronstein MM, Di\u00a0Giovanni F (2023) Drew: dynamically rewired message passing with delay. In: Krause A, Brunskill E, Cho K, Engelhardt B, Sabato S, Scarlett J (eds.) Proceedings of the 40th International Conference on Machine Learning. Proceedings of Machine Learning Research, PMLR, Honolulu, Hawaii, vol 202, pp 12252\u201312267 https:\/\/doi.org\/10.48550\/arXiv.2305.00000","DOI":"10.48550\/arXiv.2305.00000"},{"issue":"5","key":"6336_CR15","doi-asserted-by":"publisher","first-page":"911","DOI":"10.1016\/j.ipm.2016.04.001","volume":"52","author":"Y Yang","year":"2016","unstructured":"Yang Y, Xie G (2016) Efficient identification of node importance in social networks. Inf Process Manag 52(5):911\u2013922. https:\/\/doi.org\/10.1016\/j.ipm.2016.04.001","journal-title":"Inf Process Manag"},{"key":"6336_CR16","doi-asserted-by":"publisher","unstructured":"Li R, Wang S, Zhu F, Huang Q (2021) A closer look at the robustness of graph neural networks in node classification. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management (CIKM \u201921), ACM, Virtual Event, pp 3247\u20133256 https:\/\/doi.org\/10.1145\/3459637.3481957","DOI":"10.1145\/3459637.3481957"},{"key":"6336_CR17","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1146\/annurev.soc.27.1.415","volume":"27","author":"M McPherson","year":"2001","unstructured":"McPherson M, Smith-Lovin L, Cook JM (2001) Birds of a feather: homophily in social networks. Ann Rev Sociol 27:415\u2013444. https:\/\/doi.org\/10.1146\/annurev.soc.27.1.415","journal-title":"Ann Rev Sociol"},{"key":"6336_CR18","doi-asserted-by":"publisher","unstructured":"Zhu J, Yan Y, Zhao L, Heimann M, Akoglu L, Koutra D (2020) Beyond homophily in graph neural networks: current limitations and effective designs. In: Proceedings of the 34th International Conference on Neural Information Processing Systems (NeurIPS \u201920), Curran Associates Inc., pp 7793\u20137804 https:\/\/doi.org\/10.5555\/3495724.3495811","DOI":"10.5555\/3495724.3495811"},{"key":"6336_CR19","doi-asserted-by":"publisher","unstructured":"Kipf TN, Welling M (2017) Semi-supervised classification with graph convolutional networks. In; 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings https:\/\/doi.org\/10.48550\/arXiv.1609.02907","DOI":"10.48550\/arXiv.1609.02907"},{"key":"6336_CR20","doi-asserted-by":"publisher","unstructured":"Hamilton WL, Ying R, Leskovec J (2017) Inductive representation learning on large graphs. Adv Neural Inf Process Syst https:\/\/doi.org\/10.48550\/arXiv.1706.02216","DOI":"10.48550\/arXiv.1706.02216"},{"key":"6336_CR21","doi-asserted-by":"publisher","unstructured":"Brody S, Alon U, Yahav E (2022) How attentive are graph attention networks?. In: 10th International Conference on Learning Representations, ICLR 2022 - Conference Track Proceedings https:\/\/doi.org\/10.48550\/arXiv.2105.14491","DOI":"10.48550\/arXiv.2105.14491"},{"key":"6336_CR22","doi-asserted-by":"publisher","unstructured":"Morris C, Ritzert M, Fey M, Hamilton WL, Lenssen JE, Rattan G, Grohe M (2019) Weisfeiler and leman go neural: higher-order graph neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol 33, pp 4602\u20134609. https:\/\/doi.org\/10.48550\/arXiv.1810.02244","DOI":"10.48550\/arXiv.1810.02244"},{"issue":"1","key":"6336_CR23","doi-asserted-by":"publisher","first-page":"15865","DOI":"10.48550\/arXiv.2112.09992","volume":"24","author":"C Morris","year":"2023","unstructured":"Morris C, Lipman Y, Maron H, Rieck B, Kriege NM, Grohe M, Fey M, Borgwardt K (2023) Weisfeiler and leman go machine learning: the story so far. J Mach Learn Res 24(1):15865\u201315923. https:\/\/doi.org\/10.48550\/arXiv.2112.09992","journal-title":"J Mach Learn Res"},{"key":"6336_CR24","unstructured":"Barcel\u00f3 P, Kostylev EV, Monet M, P\u00e9rez J, Reutter J, Silva J-P (2020) The logical expressiveness of graph neural networks. In: 8th International Conference on Learning Representations (ICLR 2020), Virtual conference, Ethiopia. https:\/\/hal.science\/hal-03356968"},{"key":"6336_CR25","doi-asserted-by":"publisher","first-page":"19314","DOI":"10.48550\/arXiv.2006.13009","volume":"33","author":"Y Chen","year":"2020","unstructured":"Chen Y, Wu L, Zaki M (2020) Iterative deep graph learning for graph neural networks: better and robust node embeddings. Adv Neural Inf Process Syst 33:19314\u201319326. https:\/\/doi.org\/10.48550\/arXiv.2006.13009","journal-title":"Adv Neural Inf Process Syst"},{"key":"6336_CR26","doi-asserted-by":"publisher","unstructured":"Wang R, Mou S, Wang X, Xiao W, Ju Q, Shi C, Xie X (2021) Graph structure estimation neural networks. In: Proceedings of the Web Conference 2021, pp 342\u2013353. https:\/\/doi.org\/10.1145\/3442381.3449952","DOI":"10.1145\/3442381.3449952"},{"key":"6336_CR27","doi-asserted-by":"publisher","unstructured":"Zhu Y, Xu W, Zhang J, Du Y, Zhang J, Liu Q, Yang C, Wu S (2021) A survey on graph structure learning: Progress and opportunities. arXiv e-prints, p 2103 https:\/\/doi.org\/10.48550\/arXiv.2103.03036","DOI":"10.48550\/arXiv.2103.03036"},{"key":"6336_CR28","doi-asserted-by":"publisher","unstructured":"Jiang B, Zhang Z, Lin D, Tang J, Luo B (2019) Semi-supervised learning with graph learning-convolutional networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 11313\u201311320. https:\/\/doi.org\/10.1109\/CVPR.2019.01157","DOI":"10.1109\/CVPR.2019.01157"},{"key":"6336_CR29","doi-asserted-by":"publisher","unstructured":"Kim D, Oh A (2022) How to find your friendly neighborhood: Graph attention design with self-supervision. arXiv preprint arXiv:2204.04879, https:\/\/doi.org\/10.48550\/arXiv.2204.04879","DOI":"10.48550\/arXiv.2204.04879"},{"key":"6336_CR30","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3170249","author":"A Kazi","year":"2022","unstructured":"Kazi A, Cosmo L, Ahmadi S-A, Navab N, Bronstein M (2022) Differentiable graph module (dgm) for graph convolutional networks. IEEE Trans Pattern Anal Mach Intell. https:\/\/doi.org\/10.1109\/TPAMI.2022.3170249","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"6336_CR31","doi-asserted-by":"publisher","unstructured":"Klicpera J, Bojchevski A, G\u00fcnnemann S (2019) Predict then propagate: Graph neural networks meet personalized pagerank. International Conference on Learning Representations (ICLR). https:\/\/doi.org\/10.48550\/arXiv.1810.05997","DOI":"10.48550\/arXiv.1810.05997"},{"key":"6336_CR32","unstructured":"Page L, Brin S, Motwani R, Winograd T (1999) The pagerank citation ranking: bringing order to the web. In: The Web Conference. https:\/\/api.semanticscholar.org\/CorpusID:1508503"},{"key":"6336_CR33","unstructured":"Jiang B, Wang L, Tang J, Luo B (2019) Semi-supervised learning with adaptive neighborhood graph propagation network. arXiv preprint arXiv:1908.05153"},{"key":"6336_CR34","doi-asserted-by":"publisher","unstructured":"Br\u00fcel-Gabrielsson R, Yurochkin M, Solomon J (2022) Rewiring with positional encodings for graph neural networks. arXiv preprint arXiv:2201.12674, https:\/\/doi.org\/10.48550\/arXiv.2201.12674","DOI":"10.48550\/arXiv.2201.12674"},{"key":"6336_CR35","doi-asserted-by":"publisher","first-page":"28877","DOI":"10.48550\/arXiv.2106.05234","volume":"34","author":"C Ying","year":"2021","unstructured":"Ying C, Cai T, Luo S, Zheng S, Ke G, He D, Shen Y, Liu T-Y (2021) Do transformers really perform badly for graph representation? Adv Neural Inf Process Syst 34:28877\u201328888. https:\/\/doi.org\/10.48550\/arXiv.2106.05234","journal-title":"Adv Neural Inf Process Syst"},{"key":"6336_CR36","doi-asserted-by":"publisher","unstructured":"Abboud R, Dimitrov R, Ceylan II (2022) Shortest path networks for graph property prediction. In: Learning on Graphs Conference, p 5 https:\/\/doi.org\/10.48550\/arXiv.2206.01003 . PMLR","DOI":"10.48550\/arXiv.2206.01003"},{"issue":"2","key":"6336_CR37","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2022.103221","volume":"60","author":"MU Akhtar","year":"2023","unstructured":"Akhtar MU, Liu J, Liu X, Ahmed S, Cui X (2023) Nrand: an efficient and robust dismantling approach for infectious disease network. Inf Process Manag 60(2):103221. https:\/\/doi.org\/10.1016\/j.ipm.2022.103221","journal-title":"Inf Process Manag"},{"key":"6336_CR38","doi-asserted-by":"publisher","first-page":"16421","DOI":"10.48550\/arXiv.2006.16811","volume":"33","author":"Z Ma","year":"2020","unstructured":"Ma Z, Xuan J, Wang YG, Li M, Li\u00f2 P (2020) Path integral based convolution and pooling for graph neural networks. Adv Neural Inf Process Syst 33:16421\u201316433. https:\/\/doi.org\/10.48550\/arXiv.2006.16811","journal-title":"Adv Neural Inf Process Syst"},{"key":"6336_CR39","doi-asserted-by":"publisher","unstructured":"Eliasof M, Haber E, Treister E (2022) pathgcn: learning general graph spatial operators from paths. In: International Conference on Machine Learning, pp 5878\u20135891. https:\/\/doi.org\/10.48550\/arXiv.2207.07408","DOI":"10.48550\/arXiv.2207.07408"},{"key":"6336_CR40","doi-asserted-by":"publisher","unstructured":"Rong Y, Huang W, Xu T, Huang J (2020) Dropedge: Towards deep graph convolutional networks on node classification. In: International Conference on Learning Representations (ICLR). https:\/\/doi.org\/10.48550\/arXiv.1907.10903","DOI":"10.48550\/arXiv.1907.10903"},{"key":"6336_CR41","doi-asserted-by":"publisher","first-page":"22092","DOI":"10.48550\/arXiv.2005.11079","volume":"33","author":"W Feng","year":"2020","unstructured":"Feng W, Zhang J, Dong Y, Han Y, Luan H, Xu Q, Yang Q, Kharlamov E, Tang J (2020) Graph random neural networks for semi-supervised learning on graphs. Adv Neural Inf Process Syst 33:22092\u201322103. https:\/\/doi.org\/10.48550\/arXiv.2005.11079","journal-title":"Adv Neural Inf Process Syst"},{"key":"6336_CR42","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/j.inffus.2021.07.013","volume":"77","author":"Y Zhu","year":"2022","unstructured":"Zhu Y, Ma J, Yuan C, Zhu X (2022) Interpretable learning based dynamic graph convolutional networks for Alzheimer\u2019s disease analysis. Inf Fus 77:53\u201361. https:\/\/doi.org\/10.1016\/j.inffus.2021.07.013","journal-title":"Inf Fus"},{"issue":"1","key":"6336_CR43","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/TNNLS.2020.2978386","volume":"32","author":"Z Wu","year":"2021","unstructured":"Wu Z, Pan S, Chen F, Long G, Zhang C, Yu PS (2021) A comprehensive survey on graph neural networks. IEEE Trans Neural Netw Learn Syst 32(1):4\u201324. https:\/\/doi.org\/10.1109\/TNNLS.2020.2978386","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"3","key":"6336_CR44","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1038\/s42256-022-00466-8","volume":"4","author":"MH Chehreghani","year":"2022","unstructured":"Chehreghani MH (2022) Half a decade of graph convolutional networks. Nat Mach Intell 4(3):192\u2013193. https:\/\/doi.org\/10.1038\/s42256-022-00466-8","journal-title":"Nat Mach Intell"},{"issue":"9","key":"6336_CR45","doi-asserted-by":"publisher","first-page":"1371","DOI":"10.1093\/comjnl\/bxu003","volume":"57","author":"MH Chehreghani","year":"2014","unstructured":"Chehreghani MH (2014) An efficient algorithm for approximate betweenness centrality computation. Comput J 57(9):1371\u20131382. https:\/\/doi.org\/10.1093\/comjnl\/bxu003","journal-title":"Comput J"},{"issue":"5","key":"6336_CR46","doi-asserted-by":"publisher","DOI":"10.1002\/widm.1256","volume":"8","author":"S Tabassum","year":"2018","unstructured":"Tabassum S, Pereira FSF, Fernandes S, Gama J (2018) Social network analysis: an overview. WIREs Data Mining Knowl Discov 8(5):e1256. https:\/\/doi.org\/10.1002\/widm.1256","journal-title":"WIREs Data Mining Knowl Discov"},{"issue":"2","key":"6336_CR47","doi-asserted-by":"publisher","DOI":"10.1002\/widm.1393","volume":"11","author":"MH Chehreghani","year":"2021","unstructured":"Chehreghani MH (2021) Dynamical algorithms for data mining and machine learning over dynamic graphs. WIREs Data Mining Knowl Discov 11(2):e1393. https:\/\/doi.org\/10.1002\/widm.1393","journal-title":"WIREs Data Mining Knowl Discov"},{"issue":"7","key":"6336_CR48","doi-asserted-by":"publisher","first-page":"998","DOI":"10.1093\/comjnl\/bxy040","volume":"61","author":"MH Chehreghani","year":"2018","unstructured":"Chehreghani MH, Bifet A, Abdessalem T (2018) Discriminative distance-based network indices with application to link prediction. Comput J 61(7):998\u20131014. https:\/\/doi.org\/10.1093\/comjnl\/bxy040","journal-title":"Comput J"},{"key":"6336_CR49","doi-asserted-by":"crossref","unstructured":"Hagberg A, Swart P, Chult DS (2008) Exploring network structure, dynamics, and function using networkx. In: Proceedings of the 7th Python in Science Conference (SciPy2008)","DOI":"10.25080\/TCWV9851"},{"key":"6336_CR50","doi-asserted-by":"publisher","DOI":"10.1093\/acprof:oso\/9780199206650.001.0001","volume-title":"Networks: an Introduction","author":"MEJ Newman","year":"2010","unstructured":"Newman MEJ (2010) Networks: an Introduction. Oxford University Press, Oxford"},{"issue":"5","key":"6336_CR51","doi-asserted-by":"publisher","first-page":"1170","DOI":"10.1086\/228631","volume":"92","author":"P Bonacich","year":"1987","unstructured":"Bonacich P (1987) Power and centrality: a family of measures. Am J Sociol 92(5):1170\u20131182. https:\/\/doi.org\/10.1086\/228631","journal-title":"Am J Sociol"},{"key":"6336_CR52","doi-asserted-by":"publisher","unstructured":"Chen J, Ma T, Xiao C (2018) Fastgcn: fast learning with graph convolutional networks via importance sampling. arXiv preprint arXiv:1801.10247, https:\/\/doi.org\/10.48550\/arXiv.1801.10247","DOI":"10.48550\/arXiv.1801.10247"},{"key":"6336_CR53","doi-asserted-by":"publisher","unstructured":"Huang W, Zhang T, Rong Y, Huang J (2018) Adaptive sampling towards fast graph representation learning. In: Proceedings of the 32nd International Conference on Neural Information Processing Systems. NIPS\u201918, Curran Associates Inc., Red Hook, NY, USA, pp 4563\u20134572 https:\/\/doi.org\/10.48550\/arXiv.1809.05343","DOI":"10.48550\/arXiv.1809.05343"},{"key":"6336_CR54","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1002\/zamm.19290090206","volume":"9","author":"R Mises","year":"1929","unstructured":"Mises R, Pollaczek-Geiringer H (1929) Praktische verfahren der gleichungsaufl\u00f6sung. Zamm-zeitschrift Fur Angewandte Mathematik Und Mechanik 9:152\u2013164","journal-title":"Zamm-zeitschrift Fur Angewandte Mathematik Und Mechanik"},{"issue":"3","key":"6336_CR55","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1016\/S0378-8733(03)00009-1","volume":"25","author":"LA Adamic","year":"2003","unstructured":"Adamic LA, Adar E (2003) Friends and neighbors on the web. Social Netw 25(3):211\u2013230. https:\/\/doi.org\/10.1016\/S0378-8733(03)00009-1","journal-title":"Social Netw"},{"key":"6336_CR56","doi-asserted-by":"publisher","unstructured":"Wang M, Zheng D, Ye Z, Gan Q, Li M, Song X, Zhou J, Ma C, Yu L, Gai Y, Xiao T, He T, Karypis G, Li J, Zhang Z (2019) Deep graph library: A graph-centric, highly-performant package for graph neural networks. arXiv preprint arXiv:1909.01315, https:\/\/doi.org\/10.48550\/arXiv.1909.01315","DOI":"10.48550\/arXiv.1909.01315"},{"key":"6336_CR57","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1609\/aimag.v29i3.2157","volume":"29","author":"P Sen","year":"2008","unstructured":"Sen P, Namata GM, Bilgic M, Getoor L, Gallagher B, Eliassi-Rad T (2008) Collective classification in network data. AI Mag 29:93. https:\/\/doi.org\/10.1609\/aimag.v29i3.2157","journal-title":"AI Mag"},{"key":"6336_CR58","doi-asserted-by":"publisher","unstructured":"McAuley J, Targett C, Shi Q, Hengel AVD (2015) Image-based recommendations on styles and substitutes. In: SIGIR 2015 - Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval https:\/\/doi.org\/10.1145\/2766462.2767755","DOI":"10.1145\/2766462.2767755"},{"key":"6336_CR59","doi-asserted-by":"publisher","unstructured":"Hamilton W, Ying Z, Leskovec J (2017) Inductive representation learning on large graphs. Adv Neural Inf Process Syst https:\/\/doi.org\/10.48550\/arXiv.1706.02216","DOI":"10.48550\/arXiv.1706.02216"},{"key":"6336_CR60","doi-asserted-by":"publisher","DOI":"10.5555\/3327345.3327423","author":"M Zhang","year":"2018","unstructured":"Zhang M, Chen Y (2018) Link prediction based on graph neural networks. Adv Neural Inf Process Syst. https:\/\/doi.org\/10.5555\/3327345.3327423","journal-title":"Adv Neural Inf Process Syst"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-024-06336-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-024-06336-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-024-06336-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,23]],"date-time":"2024-11-23T16:13:55Z","timestamp":1732378435000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-024-06336-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,8]]},"references-count":60,"journal-issue":{"issue":"16","published-print":{"date-parts":[[2024,11]]}},"alternative-id":["6336"],"URL":"https:\/\/doi.org\/10.1007\/s11227-024-06336-x","relation":{},"ISSN":["0920-8542","1573-0484"],"issn-type":[{"value":"0920-8542","type":"print"},{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,8]]},"assertion":[{"value":"1 July 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 July 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval and consent to participate"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}]}}