{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T06:56:10Z","timestamp":1781679370069,"version":"3.54.5"},"reference-count":43,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012476","name":"Fundamental Research Funds for Central Universities of the Central South University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012476","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Neurocomputing"],"published-print":{"date-parts":[[2026,9]]},"DOI":"10.1016\/j.neucom.2026.133903","type":"journal-article","created":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T11:08:11Z","timestamp":1778756891000},"page":"133903","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Multi-Motif enhanced node representation generation for class-imbalanced node classification"],"prefix":"10.1016","volume":"695","author":[{"given":"Jiashuo","family":"Zheng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongshan","family":"Pu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ye","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.neucom.2026.133903_bib0005","series-title":"European Conference on Principles of Data Mining and Knowledge Discovery","first-page":"107","article-title":"SMOTEBoost: improving prediction of the minority class in boosting","author":"Chawla","year":"2003"},{"key":"10.1016\/j.neucom.2026.133903_bib0010","first-page":"29885","article-title":"Topology-imbalance learning for semi-supervised node classification","volume":"34","author":"Chen","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.neucom.2026.133903_bib0015","doi-asserted-by":"crossref","first-page":"1367","DOI":"10.1109\/TPAMI.2018.2832629","article-title":"Imbalanced deep learning by minority class incremental rectification","volume":"41","author":"Dong","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.neucom.2026.133903_bib0020","series-title":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","first-page":"20","article-title":"Anonymity can help minority: a novel synthetic data over-sampling strategy on multi-label graphs","author":"Duan","year":"2022"},{"key":"10.1016\/j.neucom.2026.133903_bib0025","first-page":"4125","article-title":"Heterogeneous hypergraph variational autoencoder for link prediction","volume":"44","author":"Fan","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.neucom.2026.133903_bib0030","series-title":"The World Wide Web Conference","first-page":"417","article-title":"Graph neural networks for social recommendation","author":"Fan","year":"2019"},{"key":"10.1016\/j.neucom.2026.133903_bib0035","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2021.108334","article-title":"Graph clustering via variational graph embedding","volume":"122","author":"Guo","year":"2022","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.neucom.2026.133903_bib0040","article-title":"Inductive representation learning on large graphs","volume":"30","author":"Hamilton","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.neucom.2026.133903_bib0045","author":"Hu"},{"key":"10.1016\/j.neucom.2026.133903_bib0050","doi-asserted-by":"crossref","first-page":"429","DOI":"10.3233\/IDA-2002-6504","article-title":"The class imbalance problem: a systematic study","volume":"6","author":"Japkowicz","year":"2002","journal-title":"Intell. Data Anal."},{"key":"10.1016\/j.neucom.2026.133903_bib0055","doi-asserted-by":"crossref","first-page":"1746","DOI":"10.1093\/bioinformatics\/bth163","article-title":"Efficient sampling algorithm for estimating subgraph concentrations and detecting network motifs","volume":"20","author":"Kashtan","year":"2004","journal-title":"Bioinformatics"},{"key":"10.1016\/j.neucom.2026.133903_bib0060","series-title":"Auto-encoding variational Bayes","author":"Kingma","year":"2013"},{"key":"10.1016\/j.neucom.2026.133903_bib0065","author":"Kipf"},{"key":"10.1016\/j.neucom.2026.133903_bib0070","author":"Kipf"},{"key":"10.1016\/j.neucom.2026.133903_bib0075","series-title":"Artificial Intelligence in Medicine: 8th Conference on Artificial Intelligence in Medicine in Europe, AIME 2001 Cascais, Portugal, July 1\u20134, 2001, Proceedings 8","first-page":"63","article-title":"Improving identification of difficult small classes by balancing class distribution","author":"Laurikkala","year":"2001"},{"key":"10.1016\/j.neucom.2026.133903_bib0080","series-title":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","first-page":"1328","article-title":"Graphsha: synthesizing harder samples for class-imbalanced node classification","author":"Li","year":"2023"},{"key":"10.1016\/j.neucom.2026.133903_bib0085","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2023.127229","article-title":"Graph neural network with curriculum learning for imbalanced node classification","volume":"574","author":"Li","year":"2024","journal-title":"Neurocomputing"},{"key":"10.1016\/j.neucom.2026.133903_bib0090","doi-asserted-by":"crossref","DOI":"10.1016\/j.neunet.2024.106933","article-title":"CDCGAN: class distribution-aware conditional GAN-based minority augmentation for imbalanced node classification","volume":"183","author":"Liu","year":"2025","journal-title":"Neural Netw."},{"issue":"6","key":"10.1016\/j.neucom.2026.133903_bib0095","doi-asserted-by":"crossref","first-page":"3132","DOI":"10.1109\/TKDE.2025.3549299","article-title":"A survey of imbalanced learning on graphs: problems, techniques, and future directions","volume":"37","author":"Liu","year":"2025","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.neucom.2026.133903_bib0100","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3718734","article-title":"Class-imbalanced learning on graphs: a survey","volume":"57","author":"Ma","year":"2025","journal-title":"ACM Comput. Surv."},{"key":"10.1016\/j.neucom.2026.133903_bib0105","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1146\/annurev.soc.27.1.415","article-title":"Birds of a feather: homophily in social networks","volume":"27","author":"McPherson","year":"2001","journal-title":"Annu. Rev. Sociol."},{"key":"10.1016\/j.neucom.2026.133903_bib0110","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"19493","article-title":"AutoSGNN: automatic propagation mechanism discovery for spectral graph neural networks","author":"Mo","year":"2025"},{"key":"10.1016\/j.neucom.2026.133903_bib0115","doi-asserted-by":"crossref","first-page":"1582","DOI":"10.1177\/1088467X251356798","article-title":"An inner-inter city graph neural network for predicting the course of COVID-19 cases","volume":"29","author":"Ndiaye","year":"2025","journal-title":"Intell. Data Anal."},{"key":"10.1016\/j.neucom.2026.133903_bib0120","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: synthetic minority over-sampling technique","volume":"16","author":"Nitesh","year":"2002","journal-title":"J. Artif. Intell. Res."},{"key":"10.1016\/j.neucom.2026.133903_bib0125","first-page":"141","article-title":"Method on entity identification using similarity measure based on weight of jaccard","volume":"33","author":"Pan","year":"2009","journal-title":"Beijing Jiaotong Daxue Xuebao\/J. Beijing Jiaotong Univ."},{"key":"10.1016\/j.neucom.2026.133903_bib0130","series-title":"International Conference on Learning Representations","article-title":"Graphens: neighbor-aware ego network synthesis for class-imbalanced node classification","author":"Park","year":"2021"},{"key":"10.1016\/j.neucom.2026.133903_bib0135","series-title":"Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining","first-page":"1390","article-title":"Imgagn: imbalanced network embedding via generative adversarial graph networks","author":"Qu","year":"2021"},{"key":"10.1016\/j.neucom.2026.133903_bib0140","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1007\/s10115-011-0465-6","article-title":"Smote-rs b*: a hybrid preprocessing approach based on oversampling and undersampling for high imbalanced data-sets using smote and rough sets theory","volume":"33","author":"Ramentol","year":"2012","journal-title":"Knowl. Inf. Syst."},{"key":"10.1016\/j.neucom.2026.133903_bib0145","first-page":"93","article-title":"Collective classification in network data","volume":"29","author":"Sen","year":"2008","journal-title":"AI Mag."},{"key":"10.1016\/j.neucom.2026.133903_bib0150","author":"Shchur"},{"key":"10.1016\/j.neucom.2026.133903_bib0155","series-title":"International Conference on Machine Learning","first-page":"20369","article-title":"TAM: topology-aware margin loss for class-imbalanced node classification","author":"Song","year":"2022"},{"key":"10.1016\/j.neucom.2026.133903_bib0160","series-title":"Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","first-page":"817","article-title":"Relational learning via latent social dimensions","author":"Tang","year":"2009"},{"key":"10.1016\/j.neucom.2026.133903_bib0165","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1007\/978-1-4419-6045-0_16","article-title":"Graph mining applications to social network analysis","author":"Tang","year":"2010","journal-title":"Manag. min. graph data"},{"key":"10.1016\/j.neucom.2026.133903_bib0170","author":"Veli\u010dkovi\u0107"},{"key":"10.1016\/j.neucom.2026.133903_bib0175","doi-asserted-by":"crossref","first-page":"11301","DOI":"10.1109\/TKDE.2022.3230502","article-title":"Dual structural consistency preserving community detection on social networks","volume":"35","author":"Wang","year":"2023","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.neucom.2026.133903_bib0180","doi-asserted-by":"crossref","first-page":"3402","DOI":"10.1109\/TKDE.2023.3323567","article-title":"Position matters: play a sequential game to detect significant communities","volume":"36","author":"Wang","year":"2024","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.neucom.2026.133903_bib0185","series-title":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","first-page":"519","article-title":"Graphmixup: improving class-imbalanced node classification by reinforcement mixup and self-supervised context prediction","author":"Wu","year":"2022"},{"key":"10.1016\/j.neucom.2026.133903_bib0190","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1007\/s00138-021-01251-0","article-title":"Graph neural networks in node classification: survey and evaluation","volume":"33","author":"Xiao","year":"2022","journal-title":"Mach. Vis. Appl."},{"key":"10.1016\/j.neucom.2026.133903_bib0195","author":"Yang"},{"key":"10.1016\/j.neucom.2026.133903_bib0200","series-title":"International Conference on Machine Learning","first-page":"40","article-title":"Revisiting semi-supervised learning with graph embeddings","author":"Yang","year":"2016"},{"issue":"2","key":"10.1016\/j.neucom.2026.133903_bib0205","doi-asserted-by":"crossref","first-page":"684","DOI":"10.1109\/TCYB.2024.3489605","article-title":"CA-GNN: a competence-aware graph neural network for semi-supervised learning on streaming data","volume":"55","author":"Yu","year":"2025","journal-title":"IEEE Trans. Cybern."},{"key":"10.1016\/j.neucom.2026.133903_bib0210","series-title":"Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V. 1","first-page":"2008","article-title":"Can large language models improve the adversarial robustness of graph neural networks?","author":"Zhang","year":"2025"},{"key":"10.1016\/j.neucom.2026.133903_bib0215","series-title":"Proceedings of the 14th ACM International Conference on Web Search and Data Mining","first-page":"833","article-title":"GraphSMOTE: imbalanced node classification on graphs with graph neural networks","author":"Zhao","year":"2021"}],"container-title":["Neurocomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231226013007?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231226013007?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T06:32:29Z","timestamp":1781677949000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0925231226013007"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":43,"alternative-id":["S0925231226013007"],"URL":"https:\/\/doi.org\/10.1016\/j.neucom.2026.133903","relation":{},"ISSN":["0925-2312"],"issn-type":[{"value":"0925-2312","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Multi-Motif enhanced node representation generation for class-imbalanced node classification","name":"articletitle","label":"Article Title"},{"value":"Neurocomputing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neucom.2026.133903","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":"133903"}}