{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T12:00:05Z","timestamp":1781006405126,"version":"3.54.1"},"reference-count":60,"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\/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":["Knowledge-Based Systems"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1016\/j.knosys.2026.116097","type":"journal-article","created":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T17:10:05Z","timestamp":1777569005000},"page":"116097","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["PDCE: A Plug-and-Play Dual-Channel Enhancement framework for Dynamic Graph Neural Networks"],"prefix":"10.1016","volume":"345","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-8400-5766","authenticated-orcid":false,"given":"Haishan","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3288-5195","authenticated-orcid":false,"given":"Ying","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-5089-713X","authenticated-orcid":false,"given":"Fuyuan","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-7915-2294","authenticated-orcid":false,"given":"Jinhui","family":"Ouyang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-0893-5369","authenticated-orcid":false,"given":"Nan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"4","key":"10.1016\/j.knosys.2026.116097_b1","doi-asserted-by":"crossref","first-page":"2207","DOI":"10.1007\/s10994-023-06475-x","article-title":"Temporal graph learning for dynamic link prediction with text in online social networks","volume":"113","author":"Dileo","year":"2024","journal-title":"Mach. Learn."},{"key":"10.1016\/j.knosys.2026.116097_b2","first-page":"5844","article-title":"Dynamicity-aware social bot detection with dynamic graph transformers","author":"He","year":"2024","journal-title":"IJCAI. Ijcai. Org"},{"issue":"12","key":"10.1016\/j.knosys.2026.116097_b3","doi-asserted-by":"crossref","first-page":"12081","DOI":"10.1109\/TKDE.2021.3124061","article-title":"Anomaly detection in dynamic graphs via transformer","volume":"35","author":"Liu","year":"2021","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.knosys.2026.116097_b4","first-page":"15689","article-title":"Hyperd: hybrid periodicity decoupling framework for traffic forecasting","volume":"40","author":"Shao","year":"2026"},{"key":"10.1016\/j.knosys.2026.116097_b5","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2024.108486","article-title":"A traffic flow prediction method based on constrained dynamic graph convolutional recurrent networks","volume":"133","author":"Xiao","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.knosys.2026.116097_b6","doi-asserted-by":"crossref","unstructured":"S. Zhang, T. Suzumura, L. Zhang, Dyngraphtrans: Dynamic graph embedding via modified universal transformer networks for financial transaction data, in: 2021 IEEE International Conference on Smart Data Services, SMDS, 2021, pp. 184\u2013191.","DOI":"10.1109\/SMDS53860.2021.00032"},{"issue":"5","key":"10.1016\/j.knosys.2026.116097_b7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3641857","article-title":"Fifraud: unsupervised financial fraud detection in dynamic graph streams","volume":"18","author":"Khodabandehlou","year":"2024","journal-title":"ACM Trans. Knowl. Discov. from Data"},{"key":"10.1016\/j.knosys.2026.116097_b8","series-title":"Semi-supervised classification with graph convolutional networks","author":"Kipf","year":"2016"},{"key":"10.1016\/j.knosys.2026.116097_b9","series-title":"Diffusion convolutional recurrent neural network: Data-driven traffic forecasting","author":"Li","year":"2017"},{"key":"10.1016\/j.knosys.2026.116097_b10","first-page":"17804","article-title":"Adaptive graph convolutional recurrent network for traffic forecasting","volume":"33","author":"Bai","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"9","key":"10.1016\/j.knosys.2026.116097_b11","doi-asserted-by":"crossref","first-page":"3848","DOI":"10.1109\/TITS.2019.2935152","article-title":"T-GCN: A temporal graph convolutional network for traffic prediction","volume":"21","author":"Zhao","year":"2019","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"10.1016\/j.knosys.2026.116097_b12","doi-asserted-by":"crossref","unstructured":"S. Kumar, X. Zhang, J. Leskovec, Predicting dynamic embedding trajectory in temporal interaction networks, in: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019, pp. 1269\u20131278.","DOI":"10.1145\/3292500.3330895"},{"key":"10.1016\/j.knosys.2026.116097_b13","series-title":"Tcl: Transformer-based dynamic graph modelling via contrastive learning","author":"Wang","year":"2021"},{"key":"10.1016\/j.knosys.2026.116097_b14","first-page":"67686","article-title":"Towards better dynamic graph learning: New architecture and unified library","volume":"36","author":"Yu","year":"2023","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.knosys.2026.116097_b15","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2019.06.024","article-title":"Dyngraph2vec: Capturing network dynamics using dynamic graph representation learning","volume":"187","author":"Goyal","year":"2020","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.knosys.2026.116097_b16","first-page":"1","article-title":"GC-LSTM: Graph convolution embedded LSTM for dynamic network link prediction","author":"Chen","year":"2022","journal-title":"Appl. Intell."},{"key":"10.1016\/j.knosys.2026.116097_b17","doi-asserted-by":"crossref","unstructured":"Y. Seo, M. Defferrard, P. Vandergheynst, X. Bresson, Structured sequence modeling with graph convolutional recurrent networks, in: Neural Information Processing: 25th International Conference, ICONIP 2018, Siem Reap, Cambodia, December 13-16, 2018, Proceedings, Part I 25, 2018, pp. 362\u2013373.","DOI":"10.1007\/978-3-030-04167-0_33"},{"key":"10.1016\/j.knosys.2026.116097_b18","doi-asserted-by":"crossref","unstructured":"A. Taheri, T. Berger-Wolf, Predictive temporal embedding of dynamic graphs, in: Proceedings of the 2019 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining, 2019, pp. 57\u201364.","DOI":"10.1145\/3341161.3342872"},{"key":"10.1016\/j.knosys.2026.116097_b19","doi-asserted-by":"crossref","unstructured":"J. Li, Z. Han, H. Cheng, J. Su, P. Wang, J. Zhang, L. Pan, Predicting path failure in time-evolving graphs, in: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019, pp. 1279\u20131289.","DOI":"10.1145\/3292500.3330847"},{"key":"10.1016\/j.knosys.2026.116097_b20","first-page":"4838","article-title":"Transfer graph neural networks for pandemic forecasting","volume":"vol. 35","author":"Panagopoulos","year":"2021"},{"key":"10.1016\/j.knosys.2026.116097_b21","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"7218","article-title":"Scalable spatiotemporal graph neural networks","volume":"37","author":"Cini","year":"2023"},{"key":"10.1016\/j.knosys.2026.116097_b22","doi-asserted-by":"crossref","unstructured":"J. You, T. Du, J. Leskovec, ROLAND: graph learning framework for dynamic graphs, in: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2022, pp. 2358\u20132366.","DOI":"10.1145\/3534678.3539300"},{"key":"10.1016\/j.knosys.2026.116097_b23","first-page":"32257","article-title":"Provably expressive temporal graph networks","volume":"35","author":"Souza","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.knosys.2026.116097_b24","doi-asserted-by":"crossref","unstructured":"J. Lee, S. Kim, K. Shin, Slade: Detecting dynamic anomalies in edge streams without labels via self-supervised learning, in: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024, pp. 1506\u20131517.","DOI":"10.1145\/3637528.3671845"},{"key":"10.1016\/j.knosys.2026.116097_b25","series-title":"Temporal graph networks for deep learning on dynamic graphs","author":"Rossi","year":"2020"},{"key":"10.1016\/j.knosys.2026.116097_b26","doi-asserted-by":"crossref","unstructured":"X. Wang, D. Lyu, M. Li, Y. Xia, Q. Yang, X. Wang, X. Wang, P. Cui, Y. Yang, B. Sun, et al., Apan: Asynchronous propagation attention network for real-time temporal graph embedding, in: Proceedings of the 2021 International Conference on Management of Data, 2021, pp. 2628\u20132638.","DOI":"10.1145\/3448016.3457564"},{"key":"10.1016\/j.knosys.2026.116097_b27","unstructured":"Y. Luo, P. Li, Neighborhood-aware scalable temporal network representation learning, in: Learning on Graphs Conference, 2022, p. 1."},{"key":"10.1016\/j.knosys.2026.116097_b28","doi-asserted-by":"crossref","unstructured":"G.H. Nguyen, J.B. Lee, R.A. Rossi, N.K. Ahmed, E. Koh, S. Kim, Continuous-time dynamic network embeddings, in: Companion Proceedings of the the Web Conference 2018, 2018, pp. 969\u2013976.","DOI":"10.1145\/3184558.3191526"},{"key":"10.1016\/j.knosys.2026.116097_b29","series-title":"Inductive representation learning in temporal networks via causal anonymous walks","author":"Wang","year":"2021"},{"key":"10.1016\/j.knosys.2026.116097_b30","doi-asserted-by":"crossref","first-page":"19874","DOI":"10.52202\/068431-1445","article-title":"Neural temporal walks: Motif-aware representation learning on continuous-time dynamic graphs","volume":"35","author":"Jin","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"6","key":"10.1016\/j.knosys.2026.116097_b31","doi-asserted-by":"crossref","first-page":"1332","DOI":"10.14778\/3583140.3583150","article-title":"Zebra: When temporal graph neural networks meet temporal personalized pagerank","volume":"16","author":"Li","year":"2023","journal-title":"Proc. the VLDB Endow."},{"key":"10.1016\/j.knosys.2026.116097_b32","doi-asserted-by":"crossref","unstructured":"R. Li, H. Wang, J. Piao, Q. Liao, Y. Li, Predicting Long-term Dynamics of Complex Networks via Identifying Skeleton in Hyperbolic Space, in: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024, pp. 1655\u20131666.","DOI":"10.1145\/3637528.3671968"},{"key":"10.1016\/j.knosys.2026.116097_b33","doi-asserted-by":"crossref","unstructured":"C. Zang, F. Wang, Neural dynamics on complex networks, in: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2020, pp. 892\u2013902.","DOI":"10.1145\/3394486.3403132"},{"key":"10.1016\/j.knosys.2026.116097_b34","first-page":"6367","article-title":"Graph neural controlled differential equations for traffic forecasting","volume":"vol. 36","author":"Choi","year":"2022"},{"issue":"9","key":"10.1016\/j.knosys.2026.116097_b35","doi-asserted-by":"crossref","first-page":"9168","DOI":"10.1109\/TKDE.2022.3221989","article-title":"Multivariate time series forecasting with dynamic graph neural odes","volume":"35","author":"Jin","year":"2022","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.knosys.2026.116097_b36","unstructured":"A. Gravina, D. Zambon, D. Bacciu, C. Alippi, Temporal graph ODEs for irregularly-sampled time series, in: Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, 2024, pp. 4025\u20134034."},{"key":"10.1016\/j.knosys.2026.116097_b37","unstructured":"X. Luo, J. Yuan, Z. Huang, H. Jiang, Y. Qin, W. Ju, M. Zhang, Y. Sun, Hope: High-order graph ode for modeling interacting dynamics, in: International Conference on Machine Learning, 2023, pp. 23124\u201323139."},{"key":"10.1016\/j.knosys.2026.116097_b38","unstructured":"J. Yan, Y. Feng, S. Ying, Y. Gao, Hypergraph dynamic system, in: The Twelfth International Conference on Learning Representations, 2024."},{"key":"10.1016\/j.knosys.2026.116097_b39","series-title":"Dynamic graph representation learning via self-attention networks","author":"Sankar","year":"2018"},{"key":"10.1016\/j.knosys.2026.116097_b40","series-title":"Inductive representation learning on temporal graphs","author":"Xu","year":"2020"},{"issue":"8","key":"10.1016\/j.knosys.2026.116097_b41","doi-asserted-by":"crossref","first-page":"1572","DOI":"10.14778\/3529337.3529342","article-title":"TGL: A general framework for temporal GNN training on billion-scale graphs","volume":"15","author":"Zhou","year":"2022","journal-title":"Proc. the VLDB Endow."},{"key":"10.1016\/j.knosys.2026.116097_b42","doi-asserted-by":"crossref","unstructured":"T. Zhang, J. Fang, Z. Yang, B. Cao, J. Fan, Tatkc: A temporal graph neural network for fast approximate temporal Katz centrality ranking, in: Proceedings of the ACM Web Conference 2024, 2024, pp. 527\u2013538.","DOI":"10.1145\/3589334.3645432"},{"key":"10.1016\/j.knosys.2026.116097_b43","doi-asserted-by":"crossref","unstructured":"C. Ma, Y. Ren, P. Castells, M. Sanderson, Temporal conformity-aware hawkes graph network for recommendations, in: Proceedings of the ACM Web Conference 2024, 2024, pp. 3185\u20133194.","DOI":"10.1145\/3589334.3645354"},{"key":"10.1016\/j.knosys.2026.116097_b44","doi-asserted-by":"crossref","unstructured":"Y. Wu, Y. Fang, L. Liao, On the feasibility of simple transformer for dynamic graph modeling, in: Proceedings of the ACM Web Conference 2024, 2024, pp. 870\u2013880.","DOI":"10.1145\/3589334.3645622"},{"key":"10.1016\/j.knosys.2026.116097_b45","doi-asserted-by":"crossref","unstructured":"Z. Peng, H. Liu, Y. Jia, J. Hou, Attention-driven graph clustering network, in: Proceedings of the 29th ACM International Conference on Multimedia, 2021, pp. 935\u2013943.","DOI":"10.1145\/3474085.3475276"},{"key":"10.1016\/j.knosys.2026.116097_b46","doi-asserted-by":"crossref","unstructured":"J. Guo, T. Yin, T. Zhao, J. Zhao, Y. Sun, J. Gao, Y. Wang, Improved Attributed Graph Clustering with Representation and Structure Augmentation, in: 2024 International Joint Conference on Neural Networks, IJCNN, 2024, pp. 1\u20138.","DOI":"10.1109\/IJCNN60899.2024.10650771"},{"key":"10.1016\/j.knosys.2026.116097_b47","doi-asserted-by":"crossref","DOI":"10.3389\/fphar.2024.1354540","article-title":"Prediction of multiple types of drug interactions based on multi-scale fusion and dual-view fusion","volume":"15","author":"Pan","year":"2024","journal-title":"Front. Pharmacol."},{"key":"10.1016\/j.knosys.2026.116097_b48","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 Trans. Image Process."},{"issue":"7","key":"10.1016\/j.knosys.2026.116097_b49","doi-asserted-by":"crossref","first-page":"3296","DOI":"10.1109\/TCSVT.2022.3232604","article-title":"Deep attention-guided graph clustering with dual self-supervision","volume":"33","author":"Peng","year":"2022","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.knosys.2026.116097_b50","doi-asserted-by":"crossref","unstructured":"Y. Qin, N. Pu, H. Wu, Z. Fan, Flexible Multi-view Clustering with Dynamic Views Generation, in: Proceedings of the 33rd ACM International Conference on Multimedia, 2025, pp. 1072\u20131081.","DOI":"10.1145\/3746027.3754930"},{"key":"10.1016\/j.knosys.2026.116097_b51","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2023.109833","article-title":"Graph clustering network with structure embedding enhanced","volume":"144","author":"Ding","year":"2023","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.knosys.2026.116097_b52","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2025.129764","article-title":"Towards multi-fusion graph neural network for single-cell RNA sequence clustering","volume":"631","author":"Yang","year":"2025","journal-title":"Neurocomputing"},{"key":"10.1016\/j.knosys.2026.116097_b53","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.112928","article-title":"Attention-based graph clustering network with dual information interaction","volume":"310","author":"Lin","year":"2025","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.knosys.2026.116097_b54","doi-asserted-by":"crossref","unstructured":"L. Zhang, Y. Zhao, J. Wang, Structural Embedding Contrastive Graph Clustering, in: 2024 IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA, 2024, pp. 582\u2013589.","DOI":"10.1109\/ISPA63168.2024.00080"},{"key":"10.1016\/j.knosys.2026.116097_b55","doi-asserted-by":"crossref","unstructured":"A. Li, B. Yang, H. Huo, F. Hussain, G. Xu, Hypercomplex knowledge graph-aware recommendation, in: Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2025, pp. 2017\u20132026.","DOI":"10.1145\/3726302.3730001"},{"issue":"1","key":"10.1016\/j.knosys.2026.116097_b56","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1007\/s44163-025-00723-w","article-title":"Personalized sequential recommendation via contrastive learning and knowledge graph embeddings","volume":"5","author":"Khaligh","year":"2025","journal-title":"Discov. Artif. Intell."},{"key":"10.1016\/j.knosys.2026.116097_b57","doi-asserted-by":"crossref","unstructured":"H. Yan, C. Li, J. Yin, W. Han, H. Sun, S. Wang, J. Zhang, J. Wang, MoKGNN: Boosting Graph Neural Networks via Mixture of Generic and Task-Specific Language Models, in: Proceedings of the Eighteenth ACM International Conference on Web Search and Data Mining, 2025, pp. 242\u2013250.","DOI":"10.1145\/3701551.3703571"},{"key":"10.1016\/j.knosys.2026.116097_b58","unstructured":"Y. Tian, Y. Qi, F. Guo, Freedyg: Frequency enhanced continuous-time dynamic graph model for link prediction, in: The Twelfth International Conference on Learning Representations, 2024."},{"key":"10.1016\/j.knosys.2026.116097_b59","article-title":"Convolutional neural networks on graphs with fast localized spectral filtering","volume":"29","author":"Defferrard","year":"2016","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.knosys.2026.116097_b60","first-page":"3438","article-title":"Measuring and relieving the over-smoothing problem for graph neural networks from the topological view","volume":"vol. 34","author":"Chen","year":"2020"}],"container-title":["Knowledge-Based Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705126008233?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705126008233?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T11:22:45Z","timestamp":1781004165000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0950705126008233"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":60,"alternative-id":["S0950705126008233"],"URL":"https:\/\/doi.org\/10.1016\/j.knosys.2026.116097","relation":{},"ISSN":["0950-7051"],"issn-type":[{"value":"0950-7051","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"PDCE: A Plug-and-Play Dual-Channel Enhancement framework for Dynamic Graph Neural Networks","name":"articletitle","label":"Article Title"},{"value":"Knowledge-Based Systems","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.knosys.2026.116097","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":"116097"}}