{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T10:27:20Z","timestamp":1758191240995,"version":"3.44.0"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2025,8,18]],"date-time":"2025-08-18T00:00:00Z","timestamp":1755475200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,8,18]],"date-time":"2025-08-18T00:00:00Z","timestamp":1755475200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J. King Saud Univ. Comput. Inf. Sci."],"published-print":{"date-parts":[[2025,9]]},"DOI":"10.1007\/s44443-025-00198-w","type":"journal-article","created":{"date-parts":[[2025,8,18]],"date-time":"2025-08-18T15:02:51Z","timestamp":1755529371000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["ARO-GNN: Adaptive relation-optimized graph neural networks"],"prefix":"10.1007","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4861-4514","authenticated-orcid":false,"given":"Yong","family":"Lu","sequence":"first","affiliation":[]},{"given":"Zhengguo","family":"Lin","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,8,18]]},"reference":[{"key":"198_CR1","unstructured":"Abu-El-Haija S, Perozzi B, Kapoor A, Alipourfard N, Lerman K, Harutyunyan H, Ver\u00a0Steeg G, Galstyan A (2019) Mixhop: Higher-order graph convolutional architectures via sparsified neighborhood mixing. In: International conference on machine learning, PMLR, pp 21\u201329"},{"key":"198_CR2","unstructured":"Airoldi EM, Blei D, Fienberg S, Xing E (2008) Mixed membership stochastic blockmodels. Adv Neural Inf Process Syst 21"},{"issue":"7","key":"198_CR3","first-page":"3496","volume":"44","author":"FM Bianchi","year":"2021","unstructured":"Bianchi FM, Grattarola D, Livi L, Alippi C (2021) Graph neural networks with convolutional arma filters. IEEE Trans Pattern Anal Mach Intell 44(7):3496\u20133507","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"198_CR4","unstructured":"Chen J, Ma T, Xiao C (2018) Fastgcn: fast learning with graph convolutional networks via importance sampling. arXiv preprint arXiv:1801.10247"},{"key":"198_CR5","unstructured":"Chen M, Wei Z, Huang Z, Ding B, Li Y (2020) Simple and deep graph convolutional networks. In: International conference on machine learning, PMLR, pp 1725\u20131735"},{"key":"198_CR6","unstructured":"Chien E, Peng J, Li P, Milenkovic O (2020) Adaptive universal generalized pagerank graph neural network. arXiv preprint arXiv:2006.07988"},{"issue":"1","key":"198_CR7","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1109\/TIT.1967.1053964","volume":"13","author":"T Cover","year":"1967","unstructured":"Cover T, Hart P (1967) Nearest neighbor pattern classification. IEEE Trans Inf Theory 13(1):21\u201327","journal-title":"IEEE Trans Inf Theory"},{"key":"198_CR8","unstructured":"Dai H, Kozareva Z, Dai B, Smola A, Song L (2018) Learning steady-states of iterative algorithms over graphs. In: International conference on machine learning, PMLR, pp 1106\u20131114"},{"key":"198_CR9","unstructured":"Defferrard M, Bresson X, Vandergheynst P (2016) Convolutional neural networks on graphs with fast localized spectral filtering. Advances Neural Inf Process Syst 29"},{"key":"198_CR10","first-page":"4948","volume":"44","author":"H Gao","year":"2019","unstructured":"Gao H, Ji S (2019) Graph u-nets. IEEE Trans Pattern Anal Mach Intell 44:4948\u20134960","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"198_CR11","unstructured":"Gasteiger J, Bojchevski A, G\u00fcnnemann S (2018) Predict then propagate: Graph neural networks meet personalized pagerank. arXiv preprint arXiv:1810.05997"},{"issue":"6","key":"198_CR12","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1093\/bib\/bbab159","volume":"22","author":"T Gaudelet","year":"2021","unstructured":"Gaudelet T, Day B, Jamasb AR, Soman J, Regep C, Liu G, Hayter JB, Vickers R, Roberts C, Tang J et al (2021) Utilizing graph machine learning within drug discovery and development. Briefings Bioinf 22(6):159","journal-title":"Briefings Bioinf"},{"key":"198_CR13","doi-asserted-by":"crossref","unstructured":"Giles CL, Bollacker KD, Lawrence S (1998) Citeseer: An automatic citation indexing system. In: Proceedings of the Third ACM conference on digital libraries, pp 89\u201398","DOI":"10.1145\/276675.276685"},{"key":"198_CR14","unstructured":"Gilmer J, Schoenholz SS, Riley PF, Vinyals O, Dahl GE (2017) Neural message passing for quantum chemistry. In: International conference on machine learning, PMLR, pp 1263\u20131272"},{"key":"198_CR15","doi-asserted-by":"crossref","unstructured":"Grover A, Leskovec J (2016) node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp 855\u2013864","DOI":"10.1145\/2939672.2939754"},{"key":"198_CR16","unstructured":"Hamilton W, Ying Z, Leskovec J (2017) Inductive representation learning on large graphs. Adv Neural Inf Process Syst 30"},{"key":"198_CR17","first-page":"14239","volume":"34","author":"M He","year":"2021","unstructured":"He M, Wei Z, Xu H et al (2021) Bernnet: Learning arbitrary graph spectral filters via bernstein approximation. Adv Neural Inf Process Syst 34:14239\u201314251","journal-title":"Adv Neural Inf Process Syst"},{"key":"198_CR18","unstructured":"Ingraham J, Garg V, Barzilay R, Jaakkola T (2019) Generative models for graph-based protein design. Adv Neural Inf Process Syst 32"},{"key":"198_CR19","doi-asserted-by":"crossref","unstructured":"Ju M, Hou S, Fan Y, Zhao J, Ye Y, Zhao L (2022) Adaptive kernel graph neural network. In: Proceedings of the AAAI conference on artificial intelligence, vol. 36, pp 7051\u20137058","DOI":"10.1609\/aaai.v36i6.20664"},{"key":"198_CR20","unstructured":"Kipf TN, Welling M (2016) Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907"},{"key":"198_CR21","unstructured":"Kipf TN, Welling M (2016) Variational graph auto-encoders. arXiv preprint arXiv:1611.07308"},{"issue":"5","key":"198_CR22","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1089\/big.2020.0070","volume":"8","author":"Y Li","year":"2020","unstructured":"Li Y, Qian B, Zhang X, Liu H (2020) Graph neural network-based diagnosis prediction. Big Data 8(5):379\u2013390","journal-title":"Big Data"},{"issue":"4","key":"198_CR23","doi-asserted-by":"publisher","first-page":"102011","DOI":"10.1016\/j.jksuci.2024.102011","volume":"36","author":"M Li","year":"2024","unstructured":"Li M, Meng L, Ye Z, Xiao Y, Cao S, Zhao H (2024) Line graph contrastive learning for node classification. J King Saud University-Comput Inf Sci 36(4):102011","journal-title":"J King Saud University-Comput Inf Sci"},{"key":"198_CR24","doi-asserted-by":"crossref","unstructured":"Liu M, Gao H, Ji S (2020) Towards deeper graph neural networks. In: Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, pp 338\u2013348","DOI":"10.1145\/3394486.3403076"},{"key":"198_CR25","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1023\/A:1009953814988","volume":"3","author":"AK McCallum","year":"2000","unstructured":"McCallum AK, Nigam K, Rennie J, Seymore K (2000) Automating the construction of internet portals with machine learning. Inf Retrieval 3:127\u2013163","journal-title":"Inf Retrieval"},{"key":"198_CR26","unstructured":"Mnih A, Salakhutdinov RR (2007) Probabilistic matrix factorization. Adv Neural Inf Process Syst 20"},{"key":"198_CR27","doi-asserted-by":"crossref","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","DOI":"10.1609\/aaai.v33i01.33014602"},{"key":"198_CR28","doi-asserted-by":"crossref","unstructured":"Pal A, Eksombatchai C, Zhou Y, Zhao B, Rosenberg C, Leskovec J (2020) Pinnersage: Multi-modal user embedding framework for recommendations at pinterest. In: Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, pp 2311\u20132320","DOI":"10.1145\/3394486.3403280"},{"key":"198_CR29","doi-asserted-by":"crossref","unstructured":"Perozzi B, Al-Rfou R, Skiena S (2014) Deepwalk: Online learning of social representations. In: Proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining, pp 701\u2013710","DOI":"10.1145\/2623330.2623732"},{"key":"198_CR30","doi-asserted-by":"crossref","unstructured":"Raghavan UN, Albert R, Kumara S (2007) Near linear time algorithm to detect community structures in large-scale networks. Phys Rev E\u2014Statistical, Nonlinear, Soft Matter Phys 76(3):036106","DOI":"10.1103\/PhysRevE.76.036106"},{"key":"198_CR31","unstructured":"Rubio-Madrigal C, Jamadandi A, Burkholz R (2025) Gnns getting comfy: Community and feature similarity guided rewiring. arXiv preprint arXiv:2502.04891"},{"key":"198_CR32","doi-asserted-by":"crossref","unstructured":"Tang J, Qu M, Wang M, Zhang M, Yan J, Mei Q (2015) Line: Large-scale information network embedding. In: Proceedings of the 24th International Conference on World Wide Web, pp 1067\u20131077","DOI":"10.1145\/2736277.2741093"},{"key":"198_CR33","unstructured":"Velickovic P, Cucurull G, Casanova A, Romero A, Lio P, Bengio Y et al (2017) Graph attention networks. stat 1050(20):10\u201348550"},{"key":"198_CR34","doi-asserted-by":"crossref","unstructured":"Wu J, He J, Xu J (2019) Net: Degree-specific graph neural networks for node and graph classification. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, pp 406\u2013415","DOI":"10.1145\/3292500.3330950"},{"key":"198_CR35","unstructured":"Wu F, Souza A, Zhang T, Fifty C, Yu T, Weinberger K (2019) Simplifying graph convolutional networks. In: International conference on machine learning, PMLR, pp 6861\u20136871"},{"key":"198_CR36","unstructured":"Xu K, Hu W, Leskovec J, Jegelka S (2018) How powerful are graph neural networks? arXiv preprint arXiv:1810.00826"},{"key":"198_CR37","unstructured":"Xu K, Li C, Tian Y, Sonobe T, Kawarabayashi K-i, Jegelka S (2018) Representation learning on graphs with jumping knowledge networks. In: International conference on machine learning, PMLR, pp 5453\u20135462"},{"key":"198_CR38","doi-asserted-by":"crossref","unstructured":"Yang Y, Sun Y, Wang S, Guo J, Gao J, Ju F, Yin B (2024) Graph neural networks with soft association between topology and attribute. In: Proceedings of the AAAI conference on artificial intelligence, vol. 38, pp 9260\u20139268","DOI":"10.1609\/aaai.v38i8.28778"},{"key":"198_CR39","unstructured":"You J, Ying R, Leskovec J (2019) Position-aware graph neural networks. In: International Conference on Machine Learning, PMLR, pp 7134\u20137143"},{"key":"198_CR40","doi-asserted-by":"crossref","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","DOI":"10.1609\/aaai.v32i1.11782"}],"container-title":["Journal of King Saud University Computer and Information Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44443-025-00198-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44443-025-00198-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44443-025-00198-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T12:41:31Z","timestamp":1758112891000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44443-025-00198-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,18]]},"references-count":40,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2025,9]]}},"alternative-id":["198"],"URL":"https:\/\/doi.org\/10.1007\/s44443-025-00198-w","relation":{},"ISSN":["1319-1578","2213-1248"],"issn-type":[{"type":"print","value":"1319-1578"},{"type":"electronic","value":"2213-1248"}],"subject":[],"published":{"date-parts":[[2025,8,18]]},"assertion":[{"value":"5 April 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 July 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 August 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflicts of interest related to this work.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}],"article-number":"174"}}