{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T21:05:47Z","timestamp":1776891947321,"version":"3.51.2"},"reference-count":33,"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"}],"funder":[{"DOI":"10.13039\/501100006683","name":"Xi'an Jiaotong-Liverpool University","doi-asserted-by":"publisher","award":["PGRS2006022"],"award-info":[{"award-number":["PGRS2006022"]}],"id":[{"id":"10.13039\/501100006683","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Neurocomputing"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1016\/j.neucom.2026.133420","type":"journal-article","created":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T17:17:39Z","timestamp":1774372659000},"page":"133420","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Class-incremental continual graph learning with adversarial graph condensation"],"prefix":"10.1016","volume":"682","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3336-878X","authenticated-orcid":false,"given":"QiAo","family":"Yuan","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0004-7112-5369","authenticated-orcid":false,"given":"Boxuan","family":"Zhu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3968-9752","authenticated-orcid":false,"given":"Sheng-Uei","family":"Guan","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5787-4716","authenticated-orcid":false,"given":"Ka Lok","family":"Man","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7935-7245","authenticated-orcid":false,"given":"Prudence","family":"Wong","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"1","key":"10.1016\/j.neucom.2026.133420_bib0005","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1109\/MCI.2022.3222049","article-title":"Graph lifelong learning: a survey","volume":"18","author":"Febrinanto","year":"2023","journal-title":"IEEE Comput. Intell. Mag."},{"key":"10.1016\/j.neucom.2026.133420_bib0010","first-page":"13006","article-title":"Cglb: benchmark tasks for continual graph learning","volume":"35","author":"Zhang","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.neucom.2026.133420_bib0015","series-title":"2023 IEEE International Conference on Data Mining (ICDM)","first-page":"1157","article-title":"Cat: balanced continual graph learning with graph condensation","author":"Liu","year":"2023"},{"issue":"1","key":"10.1016\/j.neucom.2026.133420_bib0020","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1109\/TKDE.2024.3485691","article-title":"Puma: efficient continual graph learning for node classification with graph condensation","volume":"37","author":"Liu","year":"2025","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.neucom.2026.133420_bib0025","author":"Redko"},{"key":"10.1016\/j.neucom.2026.133420_bib0030","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"8653","article-title":"Overcoming catastrophic forgetting in graph neural networks","volume":"vol. 35","author":"Liu","year":"2021"},{"key":"10.1016\/j.neucom.2026.133420_bib0035","series-title":"Proceedings of the 29th ACM International Conference on Information & Knowledge Management, CIKM \u201920","first-page":"2861","article-title":"Graphsail: graph structure aware incremental learning for recommender systems","author":"Xu","year":"2020"},{"key":"10.1016\/j.neucom.2026.133420_bib0040","author":"Sun"},{"key":"10.1016\/j.neucom.2026.133420_bib0045","series-title":"International Conference on Machine Learning","first-page":"32728","article-title":"Towards robust graph incremental learning on evolving graphs","author":"Su","year":"2023"},{"key":"10.1016\/j.neucom.2026.133420_bib0050","series-title":"CIKM \u201920","first-page":"1515","article-title":"Streaming graph neural networks via continual learning","author":"Wang","year":"2020"},{"key":"10.1016\/j.neucom.2026.133420_bib0055","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"4714","article-title":"Overcoming catastrophic forgetting in graph neural networks with experience replay","volume":"vol. 35","author":"Zhou","year":"2021"},{"key":"10.1016\/j.neucom.2026.133420_bib0060","series-title":"2022 IEEE International Conference on Data Mining (ICDM)","first-page":"1335","article-title":"Sparsified subgraph memory for continual graph representation learning","author":"Zhang","year":"2022"},{"key":"10.1016\/j.neucom.2026.133420_bib0065","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2025.129362","article-title":"Imcgnn: information maximization based continual graph neural networks for inductive node classification","volume":"624","author":"Yuan","year":"2025","journal-title":"Neurocomputing"},{"key":"10.1016\/j.neucom.2026.133420_bib0070","first-page":"26195","article-title":"What matters in graph class incremental learning? An information preservation perspective","volume":"37","author":"Li","year":"2024","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.neucom.2026.133420_bib0075","series-title":"Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","first-page":"4326","article-title":"Topology-aware embedding memory for continual learning on expanding networks","author":"Zhang","year":"2024"},{"key":"10.1016\/j.neucom.2026.133420_bib0080","series-title":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD \u201922","first-page":"1878","article-title":"Streaming graph neural networks with generative replay","author":"Wang","year":"2022"},{"key":"10.1016\/j.neucom.2026.133420_bib0085","series-title":"Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V","first-page":"2303","article-title":"Self-supervised continual graph learning via adaptive spaced replay on node proxies","volume":"vol. 2","author":"Peng","year":"2025"},{"key":"10.1016\/j.neucom.2026.133420_bib0090","series-title":"Proc. 2020 Conf. Empirical Methods Natural Lang. Process. (EMNLP)","first-page":"2961","article-title":"Disentangle-based continual graph representation learning","author":"Kou","year":"2020"},{"issue":"4","key":"10.1016\/j.neucom.2026.133420_bib0095","doi-asserted-by":"crossref","first-page":"4622","DOI":"10.1109\/TPAMI.2022.3186909","article-title":"Hierarchical prototype networks for continual graph representation learning","volume":"45","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.neucom.2026.133420_bib0100","first-page":"23675","article-title":"Task-free continual learning via online discrepancy distance learning","volume":"35","author":"Ye","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.neucom.2026.133420_bib0105","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.neucom.2018.05.083","article-title":"Deep visual domain adaptation: a survey","volume":"312","author":"Wang","year":"2018","journal-title":"Neurocomputing"},{"key":"10.1016\/j.neucom.2026.133420_bib0110","article-title":"Correcting sample selection bias by unlabeled data","volume":"19","author":"Huang","year":"2006","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"5","key":"10.1016\/j.neucom.2026.133420_bib0115","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3400066","article-title":"A survey of unsupervised deep domain adaptation","volume":"11","author":"Wilson","year":"2020","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"10.1016\/j.neucom.2026.133420_bib0120","article-title":"A comprehensive survey on domain adaptation for intelligent fault diagnosis","author":"Wang","year":"2025","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.neucom.2026.133420_bib0125","series-title":"Proceedings of the 29th ACM International Conference on Information & Knowledge Management","first-page":"1515","article-title":"Streaming graph neural networks via continual learning","author":"Wang","year":"2020"},{"key":"10.1016\/j.neucom.2026.133420_bib0130","article-title":"Mmd gan: towards deeper understanding of moment matching network","volume":"30","author":"Li","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.neucom.2026.133420_bib0135","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.neucom.2026.133420_bib0140","author":"Bhatia"},{"key":"10.1016\/j.neucom.2026.133420_bib0145","article-title":"Inductive representation learning on large graphs","volume":"30","author":"Hamilton","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"13","key":"10.1016\/j.neucom.2026.133420_bib0150","doi-asserted-by":"crossref","first-page":"3521","DOI":"10.1073\/pnas.1611835114","article-title":"Overcoming catastrophic forgetting in neural networks","volume":"114","author":"Kirkpatrick","year":"2017","journal-title":"Proc. Natl. Acad. Sci."},{"key":"10.1016\/j.neucom.2026.133420_bib0155","series-title":"International Conference on Learning Representations","article-title":"Semi-supervised classification with graph convolutional networks","author":"Kipf","year":"2017"},{"key":"10.1016\/j.neucom.2026.133420_bib0160","series-title":"International Conference on Learning Representations","article-title":"How powerful are graph neural networks?","author":"Xu","year":"2018"},{"issue":"7","key":"10.1016\/j.neucom.2026.133420_bib0165","doi-asserted-by":"crossref","first-page":"2361","DOI":"10.1109\/TFUZZ.2025.3567089","article-title":"Fuzzy domain adaptation via variational inference for evolving concept drift","volume":"33","author":"Wang","year":"2025","journal-title":"IEEE Trans. Fuzzy Syst."}],"container-title":["Neurocomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231226008179?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231226008179?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T20:33:52Z","timestamp":1776890032000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0925231226008179"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":33,"alternative-id":["S0925231226008179"],"URL":"https:\/\/doi.org\/10.1016\/j.neucom.2026.133420","relation":{},"ISSN":["0925-2312"],"issn-type":[{"value":"0925-2312","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Class-incremental continual graph learning with adversarial graph condensation","name":"articletitle","label":"Article Title"},{"value":"Neurocomputing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neucom.2026.133420","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":"133420"}}