{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T17:13:13Z","timestamp":1780420393871,"version":"3.54.1"},"reference-count":42,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"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"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Neural Networks"],"published-print":{"date-parts":[[2026,11]]},"DOI":"10.1016\/j.neunet.2026.109173","type":"journal-article","created":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T23:45:04Z","timestamp":1779925504000},"page":"109173","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["GAFGCN: A graph augmented fusion network for enhanced deep clustering in attributed graphs"],"prefix":"10.1016","volume":"203","author":[{"given":"Yingming","family":"Jiang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1623-9019","authenticated-orcid":false,"given":"Haiyan","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"8","key":"10.1016\/j.neunet.2026.109173_bib0001","first-page":"4110","article-title":"Graph regularized autoencoder and its application in unsupervised anomaly detection","volume":"44","author":"Ahmed","year":"2022","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"12","key":"10.1016\/j.neunet.2026.109173_bib0002","doi-asserted-by":"crossref","first-page":"8744","DOI":"10.1109\/TKDE.2024.3441766","article-title":"Make heterophilic graphs better fit GNN: A graph rewiring approach","volume":"36","author":"Bi","year":"2024","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"10.1016\/j.neunet.2026.109173_bib0003","series-title":"Proc. web conf. (WWW)","first-page":"1400","article-title":"Structural deep clustering network","author":"Bo","year":"2020"},{"issue":"5","key":"10.1016\/j.neunet.2026.109173_bib0004","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1037\/h0046049","article-title":"Structural balance: A generalization of Heider\u2019s theory","volume":"63","author":"Cartwright","year":"1956","journal-title":"Psychological Review"},{"key":"10.1016\/j.neunet.2026.109173_bib0005","series-title":"Proc. 2nd int. conf. knowl. discov. data mining (KDD\u201996)","first-page":"226","article-title":"A density-based algorithm for discovering clusters in large spatial databases with noise","author":"Ester","year":"1996"},{"issue":"6","key":"10.1016\/j.neunet.2026.109173_bib0006","doi-asserted-by":"crossref","first-page":"7792","DOI":"10.1109\/TNNLS.2022.3220914","article-title":"Deep fusion clustering network with reliable structure preservation","volume":"35","author":"Gong","year":"2024","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"10.1016\/j.neunet.2026.109173_bib0007","series-title":"Proc. 32nd ACM int. conf. inf. knowl. manage. (CIKM)","first-page":"577","article-title":"Homophily-enhanced structure learning for graph clustering","author":"Gu","year":"2023"},{"issue":"2","key":"10.1016\/j.neunet.2026.109173_bib0008","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbad020","article-title":"SGPPI: Structure-aware prediction of protein-protein interactions in rigorous conditions with graph convolutional network","volume":"24","author":"Huang","year":"2023","journal-title":"Briefings in Bioinformatics"},{"key":"10.1016\/j.neunet.2026.109173_bib0009","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.122799","article-title":"A novel nonnegative matrix factorization-based model for attributed graph clustering by incorporating complementary information","volume":"242","author":"Jannesari","year":"2024","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.neunet.2026.109173_bib0010","unstructured":"Kipf, T. N., & Welling, M. (2016). Variational graph auto-encoders. https:\/\/arxiv.org\/abs\/1611.07308."},{"key":"10.1016\/j.neunet.2026.109173_bib0011","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2024.128629","article-title":"SCGC: Self-supervised contrastive graph clustering","volume":"611","author":"Kulatilleke","year":"2025","journal-title":"Neurocomputing"},{"key":"10.1016\/j.neunet.2026.109173_bib0012","series-title":"Proc. adv. neural inf. process. syst.","first-page":"4694","article-title":"Evennet: Ignoring odd-hop neighbors improves robustness of graph neural networks","author":"Lei","year":"2022"},{"key":"10.1016\/j.neunet.2026.109173_bib0013","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":"Information Sciences"},{"key":"10.1016\/j.neunet.2026.109173_bib0014","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2024.127992","article-title":"Information-enhanced deep graph clustering network","volume":"597","author":"Liu","year":"2024","journal-title":"Neurocomputing"},{"key":"10.1016\/j.neunet.2026.109173_bib0015","series-title":"Proc. 30th ACM SIGKDD conf. knowl. discov. data mining (KDD)","first-page":"1968","article-title":"Revisiting modularity maximization for graph clustering: A contrastive learning perspective","author":"Liu","year":"2024"},{"key":"10.1016\/j.neunet.2026.109173_bib0016","series-title":"Reliable node similarity matrix guided contrastive graph clustering","first-page":"9123","volume":"36","author":"Liu","year":"2024"},{"key":"10.1016\/j.neunet.2026.109173_bib0017","series-title":"Proc. web conf. (WWW)","first-page":"1392","article-title":"Towards unsupervised deep graph structure learning","author":"Liu","year":"2022"},{"key":"10.1016\/j.neunet.2026.109173_bib0018","series-title":"Proc. AAAI conf. artif. intell.","first-page":"8914","article-title":"Hard sample aware network for contrastive deep graph clustering","author":"Liu","year":"2023"},{"key":"10.1016\/j.neunet.2026.109173_bib0019","series-title":"Proc. ACM int. conf. web search data mining (WSDM)","first-page":"779","article-title":"Learning to drop: Robust graph neural network via topological denoising","author":"Luo","year":"2021"},{"issue":"86","key":"10.1016\/j.neunet.2026.109173_bib0020","first-page":"2579","article-title":"Visualizing data using t-sne","volume":"9","author":"Van der Maaten","year":"2008","journal-title":"Journal of Machine Learning Research"},{"key":"10.1016\/j.neunet.2026.109173_bib0021","series-title":"Proc. 5th berkeley symp. math. statist. prob.","first-page":"281","article-title":"Some methods for classification and analysis of multivariate observations","author":"MacQueen","year":"1967"},{"key":"10.1016\/j.neunet.2026.109173_bib0022","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2024.102846","article-title":"Efficient self-supervised heterogeneous graph representation learning with reconstruction","volume":"117","author":"Mo","year":"2025","journal-title":"Information Fusion"},{"issue":"15","key":"10.1016\/j.neunet.2026.109173_bib0023","doi-asserted-by":"crossref","first-page":"11073","DOI":"10.1007\/s00521-023-08284-8","article-title":"Ko: Modularity optimization in community detection","volume":"35","author":"\u00d6ztemiz","year":"2023","journal-title":"Neural Computing and Applications"},{"key":"10.1016\/j.neunet.2026.109173_bib0024","series-title":"Proc. 29th ACM int. conf. multimedia (ACM MM)","first-page":"935","article-title":"Attention-driven graph clustering network","author":"Peng","year":"2021"},{"key":"10.1016\/j.neunet.2026.109173_bib0025","doi-asserted-by":"crossref","first-page":"6457","DOI":"10.1109\/TIP.2023.3333557","article-title":"EGRC-Net: Embedding-induced graph refinement clustering network","volume":"32","author":"Peng","year":"2023","journal-title":"IEEE Transactions on Image Processing"},{"issue":"6","key":"10.1016\/j.neunet.2026.109173_bib0026","doi-asserted-by":"crossref","first-page":"5472","DOI":"10.1109\/TKDE.2022.3162161","article-title":"A fast local balanced label diffusion algorithm for community detection in social networks","volume":"35","author":"Roghani","year":"2023","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"issue":"1","key":"10.1016\/j.neunet.2026.109173_bib0027","doi-asserted-by":"crossref","first-page":"1","DOI":"10.2307\/2331554","article-title":"The probable error of a mean","volume":"6","author":"Student","year":"1908","journal-title":"Biometrika"},{"key":"10.1016\/j.neunet.2026.109173_bib0028","series-title":"Proc. ACM int. conf. web search data mining (WSDM)","first-page":"665","article-title":"Rethinking and simplifying bootstrapped graph latents","author":"Sun","year":"2024"},{"issue":"5","key":"10.1016\/j.neunet.2026.109173_bib0029","doi-asserted-by":"crossref","DOI":"10.1111\/exsy.13195","article-title":"Social network analytics and visualization: Dynamic topic-based influence analysis in evolving micro-blogs","volume":"40","author":"Tabassum","year":"2023","journal-title":"Expert Systems"},{"key":"10.1016\/j.neunet.2026.109173_bib0030","series-title":"Proc. AAAI conf. artif. intell.","first-page":"9978","article-title":"Deep fusion clustering network","author":"Tu","year":"2021"},{"issue":"6","key":"10.1016\/j.neunet.2026.109173_bib0031","doi-asserted-by":"crossref","first-page":"4946","DOI":"10.1109\/TDSC.2023.3238412","article-title":"Improving cryptocurrency crime detection: Coinjoin community detection approach","volume":"20","author":"Wahrst\u00e4tter","year":"2023","journal-title":"IEEE Transactions on Dependable and Secure Computing"},{"key":"10.1016\/j.neunet.2026.109173_bib0032","series-title":"Proc. AAAI conf. artif. intell.","first-page":"10049","article-title":"Contrastive and generative graph convolutional networks for graph-based semi-supervised learning","author":"Wan","year":"2021"},{"key":"10.1016\/j.neunet.2026.109173_bib0033","series-title":"Proc. 28th int. joint conf. artif. intell. (IJCAI)","first-page":"3670","article-title":"Attributed graph clustering: A deep attentional embedding approach","author":"Wang","year":"2019"},{"key":"10.1016\/j.neunet.2026.109173_bib0034","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2023.110595","article-title":"A knowledge graph-gcn-community detection integrated model for large-scale stock price prediction","volume":"145","author":"Wang","year":"2023","journal-title":"Applied Soft Computing"},{"key":"10.1016\/j.neunet.2026.109173_bib0035","first-page":"1","article-title":"SynC: Synergistic boosting of structure and representation for deep graph clustering","author":"Wu","year":"2025","journal-title":"IEEE Transactions on Neural Networks Learning Systems"},{"issue":"6","key":"10.1016\/j.neunet.2026.109173_bib0036","doi-asserted-by":"crossref","first-page":"1448","DOI":"10.1109\/TAI.2024.3413694","article-title":"Glac-gcn: Global and local topology-aware contrastive graph clustering network","volume":"6","author":"Xu","year":"2025","journal-title":"IEEE Transactions on Artificial Intelligence"},{"issue":"6","key":"10.1016\/j.neunet.2026.109173_bib0037","doi-asserted-by":"crossref","first-page":"7257","DOI":"10.1109\/TCSS.2024.3401218","article-title":"Deep masked graph node clustering","volume":"11","author":"Yang","year":"2024","journal-title":"IEEE Transactions on Computational Social Systems"},{"key":"10.1016\/j.neunet.2026.109173_bib0038","series-title":"Proc. AAAI conf. artif. intell.","first-page":"10834","article-title":"Cluster-guided contrastive graph clustering network","author":"Yang","year":"2023"},{"issue":"6","key":"10.1016\/j.neunet.2026.109173_bib0039","doi-asserted-by":"crossref","first-page":"3312","DOI":"10.1109\/TKDE.2025.3548160","article-title":"Dual test-time training for out-of-distribution recommender system","volume":"37","author":"Yang","year":"2025","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"issue":"12","key":"10.1016\/j.neunet.2026.109173_bib0040","doi-asserted-by":"crossref","first-page":"18313","DOI":"10.1109\/TNNLS.2023.3314451","article-title":"Redundancy-free self-supervised relational learning for graph clustering","volume":"35","author":"Yi","year":"2024","journal-title":"IEEE Transactions on Neural Networks Learning Systems"},{"key":"10.1016\/j.neunet.2026.109173_bib0041","series-title":"Proc. AAAI conf. artif. intell.","first-page":"17184","article-title":"Every node is different: Dynamically fusing self-supervised tasks for attributed graph clustering","author":"Zhu","year":"2024"},{"key":"10.1016\/j.neunet.2026.109173_bib0042","series-title":"Proc. web conf. (WWW)","first-page":"2069","article-title":"Graph contrastive learning with adaptive augmentation","author":"Zhu","year":"2021"}],"container-title":["Neural Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0893608026006349?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0893608026006349?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T16:58:14Z","timestamp":1780419494000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0893608026006349"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,11]]},"references-count":42,"alternative-id":["S0893608026006349"],"URL":"https:\/\/doi.org\/10.1016\/j.neunet.2026.109173","relation":{},"ISSN":["0893-6080"],"issn-type":[{"value":"0893-6080","type":"print"}],"subject":[],"published":{"date-parts":[[2026,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"GAFGCN: A graph augmented fusion network for enhanced deep clustering in attributed graphs","name":"articletitle","label":"Article Title"},{"value":"Neural Networks","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neunet.2026.109173","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"109173"}}