{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T20:26:36Z","timestamp":1783110396009,"version":"3.54.6"},"reference-count":48,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100010814","name":"Anhui Provincial Department of Education","doi-asserted-by":"publisher","award":["2025AHGXZK10007"],"award-info":[{"award-number":["2025AHGXZK10007"]}],"id":[{"id":"10.13039\/501100010814","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61572035"],"award-info":[{"award-number":["61572035"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003995","name":"Anhui Provincial Natural Science Foundation","doi-asserted-by":"publisher","award":["2308085US11"],"award-info":[{"award-number":["2308085US11"]}],"id":[{"id":"10.13039\/501100003995","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007763","name":"Engineering Laboratory","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100007763","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Engineering Applications of Artificial Intelligence"],"published-print":{"date-parts":[[2026,8]]},"DOI":"10.1016\/j.engappai.2026.115044","type":"journal-article","created":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T06:52:59Z","timestamp":1778309579000},"page":"115044","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"P1","title":["Activity recommendation in business process modeling with dynamic graph neural network"],"prefix":"10.1016","volume":"178","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1724-1364","authenticated-orcid":false,"given":"Ziyou","family":"Gong","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xianwen","family":"Fang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.engappai.2026.115044_b1","series-title":"Aligning Observed and Modeled Behavior","author":"Adriansyah","year":"2014"},{"key":"10.1016\/j.engappai.2026.115044_b2","first-page":"1","article-title":"Knowledge graph completion for activity recommendation in business process modeling","author":"Amiri Elyasi","year":"2024","journal-title":"KI-K\u00fcnstliche Intell."},{"key":"10.1016\/j.engappai.2026.115044_b3","doi-asserted-by":"crossref","DOI":"10.1016\/j.simpa.2023.100556","article-title":"Pm4py: A process mining library for python","volume":"17","author":"Berti","year":"2023","journal-title":"Softw. Impacts"},{"key":"10.1016\/j.engappai.2026.115044_b4","doi-asserted-by":"crossref","unstructured":"Cai, D., Lam, W., 2020. Graph transformer for graph-to-sequence learning. In: Proceedings of the AAAI Conference on Artificial Intelligence. pp. 7464\u20137471.","DOI":"10.1609\/aaai.v34i05.6243"},{"key":"10.1016\/j.engappai.2026.115044_b5","doi-asserted-by":"crossref","unstructured":"Cao, B., Yin, J., Deng, S., Wang, D., Wu, Z., 2012. Graph-based workflow recommendation: on improving business process modeling. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management. pp. 1527\u20131531.","DOI":"10.1145\/2396761.2398466"},{"key":"10.1016\/j.engappai.2026.115044_b6","doi-asserted-by":"crossref","first-page":"7513","DOI":"10.1007\/s10489-021-02518-9","article-title":"Gc-lstm: Graph convolution embedded lstm for dynamic network link prediction","volume":"52","author":"Chen","year":"2022","journal-title":"Appl. Intell."},{"key":"10.1016\/j.engappai.2026.115044_b7","first-page":"1","article-title":"Statistical comparisons of classifiers over multiple data sets","volume":"7","author":"Dem\u0161ar","year":"2006","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.engappai.2026.115044_b8","doi-asserted-by":"crossref","first-page":"1380","DOI":"10.1109\/TCYB.2016.2545688","article-title":"A recommendation system to facilitate business process modeling","volume":"47","author":"Deng","year":"2016","journal-title":"IEEE Trans. Cybern."},{"key":"10.1016\/j.engappai.2026.115044_b9","article-title":"A generalization of transformer networks to graphs","author":"Dwivedi","year":"2021","journal-title":"AAAI Work. Deep. Learn. Graphs: Methods Appl."},{"key":"10.1016\/j.engappai.2026.115044_b10","series-title":"Fast graph representation learning with pytorch geometric","author":"Fey","year":"2019"},{"key":"10.1016\/j.engappai.2026.115044_b11","series-title":"Advanced Information Systems Engineering: 23rd International Conference, CAiSE 2011, London, UK, June 20-24, 2011. Proceedings 23","first-page":"482","article-title":"Process model generation from natural language text","author":"Friedrich","year":"2011"},{"key":"10.1016\/j.engappai.2026.115044_b12","series-title":"International Conference on Machine Learning","first-page":"11144","article-title":"Transformers meet directed graphs","author":"Geisler","year":"2023"},{"key":"10.1016\/j.engappai.2026.115044_b13","series-title":"International Conference on Machine Learning","first-page":"1263","article-title":"Neural message passing for quantum chemistry","author":"Gilmer","year":"2017"},{"key":"10.1016\/j.engappai.2026.115044_b14","doi-asserted-by":"crossref","first-page":"11788","DOI":"10.1109\/TNNLS.2024.3379735","article-title":"Deep learning for dynamic graphs: models and benchmarks","volume":"35","author":"Gravina","year":"2024","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.engappai.2026.115044_b15","doi-asserted-by":"crossref","unstructured":"Hussain, M.S., Zaki, M.J., Subramanian, D., 2022. Global self-attention as a replacement for graph convolution. In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. pp. 655\u2013665.","DOI":"10.1145\/3534678.3539296"},{"key":"10.1016\/j.engappai.2026.115044_b16","doi-asserted-by":"crossref","DOI":"10.1016\/j.neunet.2024.106207","article-title":"A comprehensive survey on deep graph representation learning","author":"Ju","year":"2024","journal-title":"Neural Netw."},{"key":"10.1016\/j.engappai.2026.115044_b17","doi-asserted-by":"crossref","first-page":"14582","DOI":"10.52202\/068431-1060","article-title":"Pure transformers are powerful graph learners","volume":"35","author":"Kim","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.engappai.2026.115044_b18","unstructured":"Kipf, T.N., Welling, M., 2017. Semi-supervised classification with graph convolutional networks. In: International Conference on Learning Representations."},{"key":"10.1016\/j.engappai.2026.115044_b19","first-page":"21618","article-title":"Rethinking graph transformers with spectral attention","volume":"34","author":"Kreuzer","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.engappai.2026.115044_b20","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 & Data Mining. pp. 1269\u20131278.","DOI":"10.1145\/3292500.3330895"},{"key":"10.1016\/j.engappai.2026.115044_b21","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1016\/j.infsof.2008.08.005","article-title":"Complexity metrics for workflow nets","volume":"51","author":"Lassen","year":"2009","journal-title":"Inf. Softw. Technol."},{"key":"10.1016\/j.engappai.2026.115044_b22","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1109\/TII.2013.2258677","article-title":"An efficient recommendation method for improving business process modeling","volume":"10","author":"Li","year":"2013","journal-title":"IEEE Trans. Ind. Inform."},{"key":"10.1016\/j.engappai.2026.115044_b23","doi-asserted-by":"crossref","unstructured":"Li, J., Han, Z., Cheng, H., Su, J., Wang, P., Zhang, J., Pan, L., 2019. Predicting path failure in time-evolving graphs. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. pp. 1279\u20131289.","DOI":"10.1145\/3292500.3330847"},{"key":"10.1016\/j.engappai.2026.115044_b24","series-title":"2024 IEEE 40th International Conference on Data Engineering","first-page":"2848","article-title":"Tp-gnn: Continuous dynamic graph neural network for graph classification","author":"Liu","year":"2024"},{"key":"10.1016\/j.engappai.2026.115044_b25","series-title":"Transformer for graphs: An overview from architecture perspective","author":"Min","year":"2022"},{"key":"10.1016\/j.engappai.2026.115044_b26","doi-asserted-by":"crossref","unstructured":"Morris, C., Ritzert, M., Fey, M., Hamilton, W.L., Lenssen, J.E., Rattan, G., Grohe, M., 2019. Weisfeiler and leman go neural: Higher-order graph neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence. pp. 4602\u20134609.","DOI":"10.1609\/aaai.v33i01.33014602"},{"key":"10.1016\/j.engappai.2026.115044_b27","doi-asserted-by":"crossref","unstructured":"Panagopoulos, G., Nikolentzos, G., Vazirgiannis, M., 2021. Transfer graph neural networks for pandemic forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence. pp. 4838\u20134845.","DOI":"10.1609\/aaai.v35i6.16616"},{"key":"10.1016\/j.engappai.2026.115044_b28","unstructured":"Rossi, E., Chamberlain, B., Frasca, F., Eynard, D., Monti, F., Bronstein, M., 2020. Temporal graph networks for deep learning on dynamic graphs. In: ICML 2020 Workshop on Graph Representation Learning."},{"key":"10.1016\/j.engappai.2026.115044_b29","doi-asserted-by":"crossref","unstructured":"Sankar, A., Wu, Y., Gou, L., Zhang, W., Yang, H., 2020. 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. pp. 519\u2013527.","DOI":"10.1145\/3336191.3371845"},{"key":"10.1016\/j.engappai.2026.115044_b30","doi-asserted-by":"crossref","DOI":"10.1016\/j.is.2022.102049","article-title":"Exploiting label semantics for rule-based activity recommendation in business process modeling","volume":"108","author":"Sola","year":"2022","journal-title":"Inf. Syst."},{"key":"10.1016\/j.engappai.2026.115044_b31","series-title":"European Semantic Web Conference","first-page":"316","article-title":"Activity recommendation for business process modeling with pre-trained language models","author":"Sola","year":"2023"},{"key":"10.1016\/j.engappai.2026.115044_b32","series-title":"International Conference on Business Process Management","first-page":"5","article-title":"On the use of knowledge graph completion methods for activity recommendation in business process modeling","author":"Sola","year":"2021"},{"key":"10.1016\/j.engappai.2026.115044_b33","series-title":"International Conference on Advanced Information Systems Engineering","first-page":"328","article-title":"A rule-based recommendation approach for business process modeling","author":"Sola","year":"2021"},{"key":"10.1016\/j.engappai.2026.115044_b34","series-title":"Process Mining Workshops","first-page":"453","article-title":"Sap signavio academic models: a large process model dataset","author":"Sola","year":"2022"},{"key":"10.1016\/j.engappai.2026.115044_b35","unstructured":"Trivedi, R., Farajtabar, M., Biswal, P., Zha, H., 2019. Dyrep: Learning representations over dynamic graphs. In: International Conference on Learning Representations."},{"key":"10.1016\/j.engappai.2026.115044_b36","series-title":"Process Mining - Discovery, Conformance and Enhancement of Business Processes","author":"van der Aalst","year":"2011"},{"key":"10.1016\/j.engappai.2026.115044_b37","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.engappai.2026.115044_b38","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1016\/j.ins.2014.09.057","article-title":"Prodigen: Mining complete, precise and minimal structure process models with a genetic algorithm","volume":"294","author":"V\u00e1zquez-Barreiros","year":"2015","journal-title":"Inform. Sci."},{"key":"10.1016\/j.engappai.2026.115044_b39","unstructured":"Veli\u010dkovi\u0107, P., Cucurull, G., Casanova, A., Romero, A., Li\u00f2, P., Bengio, Y., 2018. Graph attention networks. In: International Conference on Learning Representations."},{"key":"10.1016\/j.engappai.2026.115044_b40","series-title":"Inductive representation learning in temporal networks via causal anonymous walks","author":"Wang","year":"2021"},{"key":"10.1016\/j.engappai.2026.115044_b41","doi-asserted-by":"crossref","unstructured":"Wang, Y., Li, P., Bai, C., Leskovec, J., 2021b. Tedic: Neural modeling of behavioral patterns in dynamic social interaction networks. In: Proceedings of the Web Conference 2021. pp. 693\u2013705.","DOI":"10.1145\/3442381.3450096"},{"key":"10.1016\/j.engappai.2026.115044_b42","series-title":"Service-Oriented Computing: 16th International Conference, ICSOC 2018, Hangzhou, China, November 12-15, 2018, Proceedings 16","first-page":"478","article-title":"Rlrecommender: a representation-learning-based recommendation method for business process modeling","author":"Wang","year":"2018"},{"key":"10.1016\/j.engappai.2026.115044_b43","series-title":"Model collection of the business process management academic initiative","author":"Weske","year":"2020"},{"key":"10.1016\/j.engappai.2026.115044_b44","series-title":"Inductive representation learning on temporal graphs","author":"Xu","year":"2020"},{"key":"10.1016\/j.engappai.2026.115044_b45","first-page":"28877","article-title":"Do transformers really perform badly for graph representation?","volume":"34","author":"Ying","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.engappai.2026.115044_b46","doi-asserted-by":"crossref","unstructured":"You, J., Du, T., Leskovec, J., 2022. Roland: graph learning framework for dynamic graphs. In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. pp. 2358\u20132366.","DOI":"10.1145\/3534678.3539300"},{"key":"10.1016\/j.engappai.2026.115044_b47","series-title":"2020 IEEE World Congress on Services","first-page":"89","article-title":"Workflow recommendation based on graph embedding","author":"Yu","year":"2020"},{"key":"10.1016\/j.engappai.2026.115044_b48","doi-asserted-by":"crossref","DOI":"10.1007\/s11704-024-3853-2","article-title":"A survey of dynamic graph neural networks","volume":"19","author":"Zheng","year":"2025","journal-title":"Front. Comput. Sci."}],"container-title":["Engineering Applications of Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626013278?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626013278?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T20:07:12Z","timestamp":1783109232000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0952197626013278"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8]]},"references-count":48,"alternative-id":["S0952197626013278"],"URL":"https:\/\/doi.org\/10.1016\/j.engappai.2026.115044","relation":{},"ISSN":["0952-1976"],"issn-type":[{"value":"0952-1976","type":"print"}],"subject":[],"published":{"date-parts":[[2026,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Activity recommendation in business process modeling with dynamic graph neural network","name":"articletitle","label":"Article Title"},{"value":"Engineering Applications of Artificial Intelligence","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.engappai.2026.115044","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":"115044"}}