{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T13:22:24Z","timestamp":1777382544294,"version":"3.51.4"},"reference-count":34,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,3,29]],"date-time":"2026-03-29T00:00:00Z","timestamp":1774742400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Array"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.array.2026.100796","type":"journal-article","created":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T15:50:55Z","timestamp":1774885855000},"page":"100796","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Industrial chain risk monitoring with explainable dynamic knowledge graph learning"],"prefix":"10.1016","volume":"30","author":[{"given":"Yushan","family":"Guo","sequence":"first","affiliation":[]},{"given":"Yuliang","family":"Ma","sequence":"additional","affiliation":[]},{"given":"Shizhuo","family":"Deng","sequence":"additional","affiliation":[]},{"given":"Zhixin","family":"Lv","sequence":"additional","affiliation":[]},{"given":"Xiangyu","family":"Wang","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"7","key":"10.1016\/j.array.2026.100796_b1","doi-asserted-by":"crossref","first-page":"2083","DOI":"10.1080\/00207543.2016.1275873","article-title":"Simulation-based ripple effect modelling in the supply chain","volume":"55","author":"Ivanov","year":"2017","journal-title":"Int J Prod Res"},{"issue":"3","key":"10.1016\/j.array.2026.100796_b2","doi-asserted-by":"crossref","first-page":"829","DOI":"10.1080\/00207543.2018.1488086","article-title":"The impact of digital technology and industry 4.0 on the ripple effect and supply chain risk analytics","volume":"57","author":"Ivanov","year":"2019","journal-title":"Int J Prod Res"},{"issue":"10","key":"10.1016\/j.array.2026.100796_b3","doi-asserted-by":"crossref","first-page":"887","DOI":"10.1108\/09600031211281411","article-title":"Dealing with supply chain risks: linking risk management practices and strategies to performance","volume":"42","author":"Wieland","year":"2012","journal-title":"Int J Phys Distrib Logist Manage"},{"issue":"10","key":"10.1016\/j.array.2026.100796_b4","doi-asserted-by":"crossref","first-page":"1868","DOI":"10.1111\/poms.12838","article-title":"Big data analytics in operations management","volume":"27","author":"Choi","year":"2018","journal-title":"Prod Oper Manage"},{"issue":"12","key":"10.1016\/j.array.2026.100796_b5","doi-asserted-by":"crossref","first-page":"12012","DOI":"10.1109\/TKDE.2021.3118815","article-title":"A comprehensive survey on graph anomaly detection with deep learning","volume":"35","author":"Ma","year":"2021","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"10.1016\/j.array.2026.100796_b6","doi-asserted-by":"crossref","unstructured":"Su Y, Zhao Y, Niu C, Liu R, Sun W, Pei D. Robust anomaly detection for multivariate time series through stochastic recurrent neural networks. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining. 2019, p. 2828\u201337.","DOI":"10.1145\/3292500.3330672"},{"key":"10.1016\/j.array.2026.100796_b7","series-title":"LSTM-based encoder-decoder for multi-sensor anomaly detection","author":"Malhotra","year":"2016"},{"key":"10.1016\/j.array.2026.100796_b8","doi-asserted-by":"crossref","unstructured":"Audibert J, Michiardi P, Guyard F, Marti S, Zuluaga MA. Usad: Unsupervised anomaly detection on multivariate time series. In: Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining. 2020, p. 3395\u2013404.","DOI":"10.1145\/3394486.3403392"},{"key":"10.1016\/j.array.2026.100796_b9","doi-asserted-by":"crossref","unstructured":"Lee J, Kim S, Shin K. 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, p. 1506\u201317.","DOI":"10.1145\/3637528.3671845"},{"key":"10.1016\/j.array.2026.100796_b10","series-title":"2024 IEEE 40th international conference on data engineering","first-page":"2820","article-title":"Bourne: Bootstrapped self-supervised learning framework for unified graph anomaly detection","author":"Liu","year":"2024"},{"issue":"12","key":"10.1016\/j.array.2026.100796_b11","doi-asserted-by":"crossref","first-page":"2724","DOI":"10.1109\/TKDE.2017.2754499","article-title":"Knowledge graph embedding: A survey of approaches and applications","volume":"29","author":"Wang","year":"2017","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"10.1016\/j.array.2026.100796_b12","series-title":"European semantic web conference","first-page":"593","article-title":"Modeling relational data with graph convolutional networks","author":"Schlichtkrull","year":"2018"},{"key":"10.1016\/j.array.2026.100796_b13","series-title":"Advances in neural information processing systems","article-title":"Translating embeddings for modeling multi-relational data","volume":"vol. 26","author":"Bordes","year":"2013"},{"key":"10.1016\/j.array.2026.100796_b14","doi-asserted-by":"crossref","unstructured":"Huang Y, Zhang X, Zhang R, Chen J, Kim J. Progressively modality freezing for multi-modal entity alignment. In: Proceedings of the 62nd annual meeting of the association for computational linguistics (volume 1: long papers). 2024, p. 3477\u201389.","DOI":"10.18653\/v1\/2024.acl-long.190"},{"key":"10.1016\/j.array.2026.100796_b15","doi-asserted-by":"crossref","unstructured":"Lee J, Wang Y, Li J, Zhang M. Multimodal reasoning with multimodal knowledge graph. In: Proceedings of the 62nd annual meeting of the association for computational linguistics (volume 1: long papers). 2024, p. 10767\u201382.","DOI":"10.18653\/v1\/2024.acl-long.579"},{"key":"10.1016\/j.array.2026.100796_b16","series-title":"Findings of the association for computational linguistics: NAACL 2025","first-page":"5747","article-title":"Neuro-symbolic integration brings causal and reliable reasoning proofs","author":"Yang","year":"2025"},{"key":"10.1016\/j.array.2026.100796_b17","series-title":"Asia-Pacific web (aPWeb) and web-age information management (WAIM) joint international conference on web and big data","first-page":"19","article-title":"Heterogeneous graph-enhanced temporal prediction with adaptive monitoring for industrial chain data","author":"Su","year":"2025"},{"key":"10.1016\/j.array.2026.100796_b18","series-title":"Asia-Pacific web (aPWeb) and web-age information management (WAIM) joint international conference on web and big data","first-page":"681","article-title":"A novel temporal heterogeneous graph learning-based anomaly detection method for industrial chain evolution","author":"Gu","year":"2025"},{"issue":"4","key":"10.1016\/j.array.2026.100796_b19","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1007\/s11280-024-01280-5","article-title":"Multi-temporal heterogeneous graph learning with pattern-aware attention for industrial chain risk detection","volume":"27","author":"Li","year":"2024","journal-title":"World Wide Web"},{"key":"10.1016\/j.array.2026.100796_b20","series-title":"International conference on advanced data mining and applications","first-page":"222","article-title":"Knowledge graph reasoning with hierarchical attention-based temporal aggregation for industrial chain risk prediction","author":"Wang","year":"2025"},{"issue":"11","key":"10.1016\/j.array.2026.100796_b21","doi-asserted-by":"crossref","first-page":"12387","DOI":"10.1007\/s10462-023-10448-w","article-title":"Neurosymbolic ai: The 3 rd wave","volume":"56","author":"Garcez","year":"2023","journal-title":"Artif Intell Rev"},{"key":"10.1016\/j.array.2026.100796_b22","doi-asserted-by":"crossref","first-page":"193907","DOI":"10.1109\/ACCESS.2020.3031549","article-title":"Contrastive representation learning: A framework and review","volume":"8","author":"Le-Khac","year":"2020","journal-title":"IEEE Access"},{"key":"10.1016\/j.array.2026.100796_b23","doi-asserted-by":"crossref","unstructured":"Perozzi B, Al-Rfou R, Skiena S. Deepwalk: Online learning of social representations. In: Proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining. 2014, p. 701\u201310.","DOI":"10.1145\/2623330.2623732"},{"key":"10.1016\/j.array.2026.100796_b24","doi-asserted-by":"crossref","unstructured":"Grover A, Leskovec J. node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. 2016, p. 855\u201364.","DOI":"10.1145\/2939672.2939754"},{"key":"10.1016\/j.array.2026.100796_b25","doi-asserted-by":"crossref","unstructured":"Sankar A, Wu Y, Gou L, Zhang W, Yang H. Dysat: Deep neural representation learning on dynamic graphs via self-attention networks. In: Proceedings of the 13th international conference on web search and data mining. 2020, p. 519\u201327.","DOI":"10.1145\/3336191.3371845"},{"key":"10.1016\/j.array.2026.100796_b26","series-title":"Proceedings of the AAAI conference on artificial intelligence","first-page":"5363","article-title":"Evolvegcn: Evolving graph convolutional networks for dynamic graphs","volume":"vol. 34","author":"Pareja","year":"2020"},{"key":"10.1016\/j.array.2026.100796_b27","series-title":"Variational graph auto-encoders","author":"Kipf","year":"2016"},{"key":"10.1016\/j.array.2026.100796_b28","series-title":"International conference on machine learning","first-page":"8748","article-title":"Learning transferable visual models from natural language supervision","author":"Radford","year":"2021"},{"key":"10.1016\/j.array.2026.100796_b29","series-title":"Representation learning with contrastive predictive coding","author":"Oord","year":"2018"},{"key":"10.1016\/j.array.2026.100796_b30","series-title":"International conference on machine learning","first-page":"1597","article-title":"A simple framework for contrastive learning of visual representations","author":"Chen","year":"2020"},{"key":"10.1016\/j.array.2026.100796_b31","series-title":"Advances in neural information processing systems","first-page":"5812","article-title":"Graph contrastive learning with augmentations","volume":"vol. 33","author":"You","year":"2020"},{"key":"10.1016\/j.array.2026.100796_b32","doi-asserted-by":"crossref","unstructured":"Xu X, Liu C, Feng Q, Yin H, Song L, Song D. Neural network-based graph embedding for cross-platform binary code similarity detection. In: Proceedings of the 2017 ACM SIGSAC conference on computer and communications security. 2017, p. 363\u201376.","DOI":"10.1145\/3133956.3134018"},{"key":"10.1016\/j.array.2026.100796_b33","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.inffus.2019.12.012","article-title":"Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI","volume":"58","author":"Arrieta","year":"2020","journal-title":"Inf Fusion"},{"key":"10.1016\/j.array.2026.100796_b34","doi-asserted-by":"crossref","unstructured":"Haveliwala TH. Topic-sensitive pagerank. In: Proceedings of the 11th international conference on world wide web. 2002, p. 517\u201326.","DOI":"10.1145\/511446.511513"}],"container-title":["Array"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2590005626001190?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2590005626001190?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T09:24:16Z","timestamp":1777368256000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2590005626001190"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":34,"alternative-id":["S2590005626001190"],"URL":"https:\/\/doi.org\/10.1016\/j.array.2026.100796","relation":{},"ISSN":["2590-0056"],"issn-type":[{"value":"2590-0056","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Industrial chain risk monitoring with explainable dynamic knowledge graph learning","name":"articletitle","label":"Article Title"},{"value":"Array","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.array.2026.100796","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Authors. Published by Elsevier Inc.","name":"copyright","label":"Copyright"}],"article-number":"100796"}}