{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T10:58:38Z","timestamp":1774522718315,"version":"3.50.1"},"reference-count":47,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T00:00:00Z","timestamp":1771286400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T00:00:00Z","timestamp":1771286400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62172160"],"award-info":[{"award-number":["62172160"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100004479","name":"Jiangxi Provincial Natural Science Foundation","doi-asserted-by":"crossref","award":["20212ACB212002"],"award-info":[{"award-number":["20212ACB212002"]}],"id":[{"id":"10.13039\/501100004479","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2026,3]]},"DOI":"10.1007\/s13042-025-02936-y","type":"journal-article","created":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T11:24:44Z","timestamp":1771327484000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["EvolveTrust: evolving graph neural network for trust assessment in time-aware social networks"],"prefix":"10.1007","volume":"17","author":[{"given":"Jie","family":"Wen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nan","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hualin","family":"Zhan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,2,17]]},"reference":[{"key":"2936_CR1","doi-asserted-by":"crossref","unstructured":"Yuan W, Nguyen QVH, He T, Chen L, Yin H (2023) Manipulating federated recommender systems: Poisoning with synthetic users and its countermeasures. In: Proceedings of the 46th international ACM SIGIR conference on research and development in information retrieval, pp 1690\u20131699","DOI":"10.1145\/3539618.3591722"},{"issue":"C","key":"2936_CR2","first-page":"184","volume":"75","author":"H Ferry","year":"2015","unstructured":"Ferry H, Kris B, Ryan C (2015) Reputation systems. J Parallel Distrib Comput 75(C):184\u2013197","journal-title":"J Parallel Distrib Comput"},{"key":"2936_CR3","doi-asserted-by":"crossref","unstructured":"Liu G, Chen Q, Yang Q, Zhu B, Wang H, Wang W (2017) Opinionwalk: An efficient solution to massive trust assessment in online social networks. In: IEEE INFOCOM 2017\u2014IEEE conference on computer communications, pp 1\u20139","DOI":"10.1109\/INFOCOM.2017.8057106"},{"key":"2936_CR4","doi-asserted-by":"crossref","unstructured":"Liu G, Yang Q, Wang H, Lin X, Wittie MP (2014) Assessment of multi-hop interpersonal trust in social networks by three-valued subjective logic. In: IEEE INFOCOM 2014\u2014IEEE conference on computer communications, pp 1698\u20131706","DOI":"10.1109\/INFOCOM.2014.6848107"},{"key":"2936_CR5","unstructured":"Paolo M, Paolo A (2005) Controversial users demand local trust metrics: an experimental study on epinions. com community. Proceedings of the National Conference on Artificial Intelligence, 20, 121"},{"key":"2936_CR6","doi-asserted-by":"crossref","unstructured":"Zoltan G, Hector G-M, Jan P (2004) Combating web spam with trustrank. In: Proceedings of the thirtieth international conference on very large data bases (VLDB 2004), pp 576\u2013587","DOI":"10.1016\/B978-012088469-8\/50052-8"},{"key":"2936_CR7","doi-asserted-by":"crossref","unstructured":"Jennifer G, Hendler J (2006) Filmtrust: movie recommendations using trust in web-based social networks. In: 2006 3rd IEEE consumer communications and networking conference (CCNC 2006), 1, 282\u2013286","DOI":"10.1109\/CCNC.2006.1593032"},{"key":"2936_CR8","doi-asserted-by":"crossref","unstructured":"Li Z, Fang W, Zhu C, Chen W, Hao T, Zhang W (2024) Trust evaluation with deep learning in online social networks: a state-of-the-art review. In: Advanced intelligent computing technology and applications, pp 3\u201312","DOI":"10.1007\/978-981-97-5588-2_1"},{"key":"2936_CR9","doi-asserted-by":"crossref","unstructured":"Liu G, Li C, Yang Q (2019) Neuralwalk: tyrust assessment in online social networks with neural networks. In: IEEE INFOCOM 2019\u2014IEEE conference on computer communications, pp 1\u20139","DOI":"10.1109\/INFOCOM.2019.8737469"},{"key":"2936_CR10","doi-asserted-by":"crossref","unstructured":"Lin W, Gao Z, Li B (2020) Guardian: evaluating trust in online social networks with graph convolutional networks. In: IEEE INFOCOM 2020\u2014IEEE conference on computer communications, pp 914\u2013923","DOI":"10.1109\/INFOCOM41043.2020.9155370"},{"key":"2936_CR11","doi-asserted-by":"crossref","unstructured":"Lin W, Li B (2021) Medley: predicting social trust in time-varying online social networks. In: IEEE INFOCOM 2021\u2014IEEE conference on computer communications, pp 1\u201310","DOI":"10.1109\/INFOCOM42981.2021.9488814"},{"key":"2936_CR12","doi-asserted-by":"crossref","unstructured":"Jie W, Nan J, Jin L, Ximeng L, Honglong C, Yanzhi R, Zhaohui Y, Ziang T (2023) Dtrust: toward dynamic trust levels assessment in time-varying online social networks. In: IEEE INFOCOM 2023\u2014IEEE conference on computer communications, pp 1\u201310","DOI":"10.1109\/INFOCOM53939.2023.10228962"},{"key":"2936_CR13","doi-asserted-by":"crossref","unstructured":"Pareja A, Domeniconi G, Chen J, Ma T, Suzumura T, Kanezashi H, Kaler T, Schardl TB, Leiserson CE (2020) Evolvegcn: evolving graph convolutional networks for dynamic graphs. In: Proceedings of the thirty-fourth AAAI conference on artificial intelligence (AAAI 2020), pp 678\u2013686","DOI":"10.1609\/aaai.v34i04.5984"},{"key":"2936_CR14","doi-asserted-by":"crossref","unstructured":"Simonovsky M, Komodakis N (2017) Dynamic edge-conditioned filters in convolutional neural networks on graphs. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR 2017), pp 29\u201338","DOI":"10.1109\/CVPR.2017.11"},{"key":"2936_CR15","unstructured":"Justin G, Samuel SS, Patrick FR, Oriol V, George ED (2017) Neural message passing for quantum chemistry. In: Proceedings of the 34th international conference on machine learning (ICML 2017), 70, 1263\u20131272"},{"key":"2936_CR16","doi-asserted-by":"crossref","unstructured":"Cho K, Merrienboer B, Gulcehre C, Bougares F, Schwenk H, Bengio Y (2014) Learning phrase representations using rnn encoder\u2013decoder for statistical machine translation. In: EMNLP2014","DOI":"10.3115\/v1\/D14-1179"},{"key":"2936_CR17","unstructured":"Bruna J, Zaremba W, Szlam A, LeCun Y (2014) Spectral networks and locally connected networks on graphs. In: Proceedings of the international conference on learning representations (ICLR)"},{"key":"2936_CR18","unstructured":"Henaff M, Bruna J, Lecun Y (2015) Deep convolutional networks on graph-structured data. In: Proceedings of the 29th international conference on neural information processing systems (NIPS 2016), pp 2125\u20132133"},{"key":"2936_CR19","unstructured":"Petar V, Guillem C, Arantxa C, Adriana R, Pietro L, Yoshua B (2018) Graph attention networks. In: Proceedings of the international conference on learning representations (ICLR)"},{"key":"2936_CR20","unstructured":"Zhang J, Shi X, Xie J, Ma H, King I, Yeung D-Y (2018) Gaan: gated attention networks for learning on large and spatiotemporal graphs. In: Proceedings of the uncertainty in artificial intelligence, pp 339\u2013349"},{"key":"2936_CR21","doi-asserted-by":"crossref","unstructured":"Kumar S, Zhang X, Leskovec J (2019) Predicting dynamic embedding trajectory in temporal interaction networks. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery and data mining, pp 1269\u20131278","DOI":"10.1145\/3292500.3330895"},{"issue":"4","key":"2936_CR22","doi-asserted-by":"publisher","first-page":"4668","DOI":"10.1109\/TNNLS.2022.3184967","volume":"35","author":"M Tiezzi","year":"2024","unstructured":"Tiezzi M, Ciravegna G, Gori M (2024) Graph neural networks for graph drawing. IEEE Trans Neural Netw Learn Syst 35(4):4668\u20134681","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"12","key":"2936_CR23","doi-asserted-by":"publisher","first-page":"9109","DOI":"10.1109\/TKDE.2024.3466508","volume":"36","author":"X Luo","year":"2024","unstructured":"Luo X, Zhang S, Wu J, Chen H, Peng H, Zhou C, Li Z, Xue S, Yang J (2024) Reipool: reinforced pooling graph neural networks for graph-level representation learning. IEEE Trans Knowl Data Eng 36(12):9109\u20139122","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"2936_CR24","unstructured":"Yuan W, Ye G, Zhao X, Nguyen QVH, Cao Y, Yin H (2024) Tackling data heterogeneity in federated time series forecasting. arXiv preprint arXiv:2411.15716"},{"key":"2936_CR25","unstructured":"Xu D, Ruan C, korpeoglu E, Kumar S, Achan K (2020) Inductive representation learning on temporal graphs. In: International conference on learning representations (iclr2020), pp 1\u201317"},{"key":"2936_CR26","first-page":"89","volume":"63","author":"P Hao","year":"2020","unstructured":"Hao P, Jianxin L, Hao Y, Qiran G, Senzhang W, Lin L, Lihong W, Xiang R (2020) Dynamic network embedding via incremental skip-gram with negative sampling. Sci China Inf Sci 63:89\u2013107","journal-title":"Sci China Inf Sci"},{"issue":"3","key":"2936_CR27","doi-asserted-by":"publisher","first-page":"628","DOI":"10.1109\/TC.2021.3057082","volume":"71","author":"H Peng","year":"2021","unstructured":"Peng H, Yang R, Wang Z, Li J, He L, Philip SY, Zomaya AY, Ranjan R (2021) Lime: low-cost and incremental learning for dynamic heterogeneous information networks. IEEE Trans Comput 71(3):628\u2013642","journal-title":"IEEE Trans Comput"},{"key":"2936_CR28","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1016\/j.artint.2024.104175","volume":"334","author":"R Yang","year":"2024","unstructured":"Yang R, Peng H, Liu C, Li A (2024) Incremental measurement of structural entropy for dynamic graphs. Artif Intell 334:104\u2013175","journal-title":"Artif Intell"},{"key":"2936_CR29","doi-asserted-by":"crossref","unstructured":"Liu J, Liu J, Zhao K, Tang Y, Chen W (2024) Tp-gnn: continuous dynamic graph neural network for graph classification. In: 2024 IEEE 40th international conference on data engineering (ICDE), pp 2848\u20132861","DOI":"10.1109\/ICDE60146.2024.00215"},{"key":"2936_CR30","unstructured":"Wang Y, Singh MP (2006) Trust representation and aggregation in a distributed agent system. In: Proceedings of the 21st National Conference on Artificial Intelligence (AAAI 2006), pp 1425\u20131430"},{"issue":"2","key":"2936_CR31","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1109\/TSC.2011.58","volume":"6","author":"G Liu","year":"2013","unstructured":"Liu G, Wang Y, Orgun MA, Lim E-P (2013) Finding the optimal social trust path for the selection of trustworthy service providers in complex social networks. IEEE Trans Serv Comput 6(2):152\u2013167","journal-title":"IEEE Trans Serv Comput"},{"key":"2936_CR32","doi-asserted-by":"crossref","unstructured":"Mui L, Mohtashemi M, Halberstadt A (2002) A computational model of trust and reputation. In: Proceedings of the 35th annual Hawaii international conference on system sciences, pp 2431\u20132439","DOI":"10.1109\/HICSS.2002.994181"},{"key":"2936_CR33","doi-asserted-by":"crossref","unstructured":"Josang A, Bhuiyan T (2008) Optimal trust network analysis with subjective logic. In: Proceedings of the 2008 second international conference on emerging security information, systems and technologies, pp 179\u2013184","DOI":"10.1109\/SECURWARE.2008.64"},{"issue":"3","key":"2936_CR34","first-page":"212","volume":"9","author":"J Audun","year":"2001","unstructured":"Audun J (2001) A logic for uncertain probabilities. Int J Uncertain Fuzz Knowl-Based Syst 9(3):212\u2013279","journal-title":"Int J Uncertain Fuzz Knowl-Based Syst"},{"key":"2936_CR35","doi-asserted-by":"crossref","unstructured":"Shafer G (1976) A mathematical theory of evidence vol. 42. Princeton University Press","DOI":"10.1515\/9780691214696"},{"key":"2936_CR36","doi-asserted-by":"crossref","unstructured":"Zhao K, Pan L (2014) A machine learning based trust evaluation framework for online social networks. In: Proceedings of the 2014 IEEE 13th international conference on trust, security and privacy in computing and communications, pp 69\u201374","DOI":"10.1109\/TrustCom.2014.13"},{"issue":"4","key":"2936_CR37","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3323163","volume":"19","author":"PD Meo","year":"2019","unstructured":"Meo PD (2019) Trust prediction via matrix factorisation. ACM Trans Internet Technol 19(4):1\u201320","journal-title":"ACM Trans Internet Technol"},{"key":"2936_CR38","doi-asserted-by":"crossref","unstructured":"Yao Y, Tong H, Yan X, Xu F, Lu J (2013) Matri: a multi-aspect and transitive trust inference model. In: Proceedings of the international world wide web conference committee, pp 1467\u20131476","DOI":"10.1145\/2488388.2488516"},{"key":"2936_CR39","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1016\/j.knosys.2023.111119","volume":"282","author":"Y Xu","year":"2023","unstructured":"Xu Y, Feng Z, Xing M, Wu H, Chen S, Xue X, Dustdar S (2023) Metapath-guided multi-headed attention networks for trust prediction in heterogeneous social networks. Knowl-Based Syst 282:111\u2013119","journal-title":"Knowl-Based Syst"},{"issue":"6","key":"2936_CR40","first-page":"5865","volume":"35","author":"N Jiang","year":"2023","unstructured":"Jiang N, Jie W, Li J, Liu X, Jin D (2023) Gatrust: a multi-aspect graph attention network model for trust assessment in osns. IEEE Trans Knowl Data Eng 35(6):5865\u20135878","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"2936_CR41","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 (KDD 2014), pp 701\u2013710","DOI":"10.1145\/2623330.2623732"},{"issue":"6","key":"2936_CR42","doi-asserted-by":"publisher","first-page":"2767","DOI":"10.1109\/TNNLS.2021.3107948","volume":"34","author":"Q Wang","year":"2021","unstructured":"Wang Q, Zhao W, Yang J, Wu J, Xue S, Xing Q, Philip SY (2021) C-deeptrust: a context-aware deep trust prediction model in online social networks. IEEE Trans Neural Netw Learn Syst 34(6):2767\u20132780","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"9","key":"2936_CR43","doi-asserted-by":"publisher","first-page":"3848","DOI":"10.1109\/TITS.2019.2935152","volume":"21","author":"L Zhao","year":"2020","unstructured":"Zhao L, Song Y, Zhang C, Liu Y, Wang P, Lin T, Deng M, Li H (2020) T-gcn: a temporal graph convolutional network for traffic prediction. IEEE Trans Intell Transp Syst 21(9):3848\u20133858","journal-title":"IEEE Trans Intell Transp Syst"},{"issue":"5","key":"2936_CR44","doi-asserted-by":"publisher","first-page":"4433","DOI":"10.1109\/TDSC.2024.3353548","volume":"21","author":"J Wang","year":"2024","unstructured":"Wang J, Yan Z, Lan J, Bertino E, Pedrycz W (2024) Trustguard: Gnn-based robust and explainable trust evaluation with dynamicity support. IEEE Trans Dependable Secure Comput 21(5):4433\u20134450","journal-title":"IEEE Trans Dependable Secure Comput"},{"key":"2936_CR45","doi-asserted-by":"publisher","first-page":"79143","DOI":"10.1109\/ACCESS.2021.3082932","volume":"9","author":"J Skarding","year":"2021","unstructured":"Skarding J, Gabrys B, Musial K (2021) Foundations and modeling of dynamic networks using dynamic graph neural networks: a survey. IEEE Access 9:79143\u201379168","journal-title":"IEEE Access"},{"key":"2936_CR46","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"},{"issue":"8","key":"2936_CR47","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735\u20131780","journal-title":"Neural Comput"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-025-02936-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-025-02936-y","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-025-02936-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T10:01:51Z","timestamp":1774519311000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-025-02936-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,17]]},"references-count":47,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2026,3]]}},"alternative-id":["2936"],"URL":"https:\/\/doi.org\/10.1007\/s13042-025-02936-y","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,17]]},"assertion":[{"value":"23 January 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 October 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 February 2026","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 no Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"133"}}