{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T02:15:34Z","timestamp":1774404934668,"version":"3.50.1"},"reference-count":60,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"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","award":["61671095"],"award-info":[{"award-number":["61671095"]}],"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":[[2025,11]]},"DOI":"10.1016\/j.knosys.2025.114747","type":"journal-article","created":{"date-parts":[[2025,10,23]],"date-time":"2025-10-23T01:47:15Z","timestamp":1761184035000},"page":"114747","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":1,"special_numbering":"PC","title":["Heterogeneous network representation learning via multiple attention mechanisms"],"prefix":"10.1016","volume":"330","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-1524-6905","authenticated-orcid":false,"given":"Lili","family":"Han","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7449-9057","authenticated-orcid":false,"given":"Hui","family":"Zhao","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"5","key":"10.1016\/j.knosys.2025.114747_bib0001","first-page":"5269","article-title":"Short text topic learning using heterogeneous information network","volume":"35","author":"Wang","year":"2023","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"3","key":"10.1016\/j.knosys.2025.114747_bib0002","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3385415","article-title":"Network embedding for community detection in attributed networks","volume":"14","author":"Sun","year":"2020","journal-title":"ACM Trans. Knowl. Discov. Data"},{"key":"10.1016\/j.knosys.2025.114747_bib0003","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2021.106817","article-title":"HeteroGraphRec: a heterogeneous graph-based neural networks for social recommendations","volume":"217","author":"Salamat","year":"2021","journal-title":"Knowl. Based Syst."},{"issue":"11","key":"10.1016\/j.knosys.2025.114747_bib0004","doi-asserted-by":"crossref","first-page":"3425","DOI":"10.1007\/s13042-022-01605-8","article-title":"RLIM: representation learning method for influence maximization in social networks","volume":"13","author":"Sun","year":"2022","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"10.1016\/j.knosys.2025.114747_bib0005","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.120115","article-title":"OSGNN: original graph and subgraph aggregated graph neural network","volume":"225","author":"Yan","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.knosys.2025.114747_bib0006","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2025.112977","article-title":"AGHINT: attribute-guided representation learning on heterogeneous information networks with transformer","volume":"310","author":"Yuan","year":"2025","journal-title":"Knowl. Based Syst."},{"issue":"1","key":"10.1016\/j.knosys.2025.114747_bib0007","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1007\/s13042-022-01689-2","article-title":"Multi-view bayesian spatiotemporal graph neural networks for reliable traffic flow prediction","volume":"15","author":"Xia","year":"2024","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"10.1016\/j.knosys.2025.114747_bib0008","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2022.117921","article-title":"Graph neural network for traffic forecasting: a survey","volume":"207","author":"Jiang","year":"2022","journal-title":"Expert Syst. Appl."},{"issue":"3","key":"10.1016\/j.knosys.2025.114747_bib0009","first-page":"2641","article-title":"Hierarchical representation learning for attributed networks","volume":"35","author":"Zhao","year":"2023","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"2","key":"10.1016\/j.knosys.2025.114747_bib0010","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1109\/TBDATA.2022.3177455","article-title":"A survey on heterogeneous graph embedding: methods, techniques, applications and sources","volume":"9","author":"Wang","year":"2023","journal-title":"IEEE Trans. Big Data"},{"issue":"1","key":"10.1016\/j.knosys.2025.114747_bib0011","doi-asserted-by":"crossref","first-page":"829","DOI":"10.1109\/TCSS.2023.3239034","article-title":"A heterogeneous graph neural network with attribute enhancement and structure-aware attention","volume":"11","author":"Fan","year":"2024","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"issue":"2","key":"10.1016\/j.knosys.2025.114747_bib0012","doi-asserted-by":"crossref","first-page":"803","DOI":"10.1007\/s13042-024-02294-1","article-title":"Multi-graph aggregated graph neural network for heterogeneous graph representation learning","volume":"16","author":"Zhu","year":"2025","journal-title":"Int. J. Mach. Learn. Cybern."},{"issue":"10","key":"10.1016\/j.knosys.2025.114747_bib0013","doi-asserted-by":"crossref","DOI":"10.1016\/j.jksuci.2023.101855","article-title":"Multi-view learning-based heterogeneous network representation learning","volume":"35","author":"Chen","year":"2023","journal-title":"J. King Saud Univ. Comput. Inf. Sci."},{"issue":"6","key":"10.1016\/j.knosys.2025.114747_bib0014","doi-asserted-by":"crossref","first-page":"1553","DOI":"10.1007\/s13042-021-01465-8","article-title":"Meta-path-based heterogeneous graph neural networks in academic network","volume":"13","author":"Liang","year":"2022","journal-title":"Int. J. Mach. Learn. Cybern."},{"issue":"20","key":"10.1016\/j.knosys.2025.114747_bib0015","doi-asserted-by":"crossref","first-page":"10055","DOI":"10.1007\/s10489-024-05703-8","article-title":"Semi-supervised heterogeneous graph contrastive learning with label-guided","volume":"54","author":"Li","year":"2024","journal-title":"Appl. Intell."},{"issue":"1","key":"10.1016\/j.knosys.2025.114747_bib0016","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3616377","article-title":"Adaptive neighbor graph aggregated graph attention network for heterogeneous graph embedding","volume":"18","author":"Kaibiao","year":"2023","journal-title":"ACM Trans. Knowl. Discov. Data"},{"key":"10.1016\/j.knosys.2025.114747_bib0017","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.aiopen.2021.01.001","article-title":"Graph neural networks: a review of methods and applications","volume":"1","author":"Zhou","year":"2020","journal-title":"AI Open"},{"issue":"1","key":"10.1016\/j.knosys.2025.114747_bib0018","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1109\/TNN.2008.2005605","article-title":"The graph neural network model","volume":"20","author":"Scarselli","year":"2009","journal-title":"IEEE Trans. Neural Netw."},{"issue":"5","key":"10.1016\/j.knosys.2025.114747_bib0019","first-page":"5782","article-title":"Explainability in graph neural networks: a taxonomic survey","volume":"45","author":"Yuan","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.knosys.2025.114747_bib0020","series-title":"Proceedings of the 17th ACM International Conference on Web Search and Data Mining","first-page":"976","article-title":"RDGCN: reinforced dependency graph convolutional network for aspect-based sentiment analysis","author":"Zhao","year":"2024"},{"key":"10.1016\/j.knosys.2025.114747_bib0021","series-title":"Proceedings of the 5th International Conference on Learning Representations","first-page":"548","article-title":"Semi-supervised classification with graph convolutional networks","author":"Kipf","year":"2017"},{"key":"10.1016\/j.knosys.2025.114747_bib0022","series-title":"Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","first-page":"1243","article-title":"AM-GCN: adaptive multi-channel graph convolutional networks","author":"Wang","year":"2020"},{"issue":"6","key":"10.1016\/j.knosys.2025.114747_bib0023","doi-asserted-by":"crossref","first-page":"7411","DOI":"10.1109\/TCSS.2024.3405569","article-title":"LIHAN: a lattice-guided incomplete heterogeneous information network embedding model for node classification","volume":"11","author":"Mei","year":"2024","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"issue":"10","key":"10.1016\/j.knosys.2025.114747_bib0024","doi-asserted-by":"crossref","first-page":"4854","DOI":"10.1109\/TKDE.2020.3045924","article-title":"Heterogeneous network representation learning: a unified framework with survey and benchmark","volume":"34","author":"Yang","year":"2022","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"8","key":"10.1016\/j.knosys.2025.114747_bib0025","doi-asserted-by":"crossref","first-page":"8003","DOI":"10.1007\/s10462-022-10375-2","article-title":"Heterogeneous graph neural networks analysis: a survey of techniques, evaluations and applications","volume":"56","author":"Bing","year":"2023","journal-title":"Artif. Intell. Rev."},{"key":"10.1016\/j.knosys.2025.114747_bib0026","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2021.107611","article-title":"MEGNN: meta-path extracted graph neural network for heterogeneous graph representation learning","volume":"235","author":"Chang","year":"2022","journal-title":"Knowl. Based Syst."},{"key":"10.1016\/j.knosys.2025.114747_bib0027","series-title":"Proceedings of the ACM Web Conference","first-page":"1631","article-title":"Collaborative knowledge distillation for heterogeneous information network embedding","author":"Wang","year":"2022"},{"key":"10.1016\/j.knosys.2025.114747_bib0028","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2022.109673","article-title":"Collaborative representation learning for nodes and relations via heterogeneous graph neural network","volume":"255","author":"Li","year":"2022","journal-title":"Knowl. Based Syst."},{"issue":"4","key":"10.1016\/j.knosys.2025.114747_bib0029","doi-asserted-by":"crossref","first-page":"3357","DOI":"10.1007\/s00521-022-07862-6","article-title":"Multimodal heterogeneous graph attention network","volume":"35","author":"Jia","year":"2023","journal-title":"Neural Comput. Appl."},{"issue":"9","key":"10.1016\/j.knosys.2025.114747_bib0030","doi-asserted-by":"crossref","first-page":"9054","DOI":"10.1109\/TKDE.2022.3221099","article-title":"Learning bi-typed multi-relational heterogeneous graph via dual hierarchical attention networks","volume":"35","author":"Zhao","year":"2023","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"5500","key":"10.1016\/j.knosys.2025.114747_bib0031","doi-asserted-by":"crossref","first-page":"2323","DOI":"10.1126\/science.290.5500.2323","article-title":"Nonlinear dimensionality reduction by locally linear embedding","volume":"290","author":"Roweis","year":"2000","journal-title":"Science"},{"issue":"5552","key":"10.1016\/j.knosys.2025.114747_bib0032","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1126\/science.295.5552.7a","article-title":"The isomap algorithm and topological stability","volume":"295","author":"Balasubramanian","year":"2002","journal-title":"Science"},{"key":"10.1016\/j.knosys.2025.114747_bib0033","series-title":"Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","first-page":"701","article-title":"Deepwalk: online learning of social representations","author":"Perozzi","year":"2014"},{"key":"10.1016\/j.knosys.2025.114747_bib0034","series-title":"Proceedings of the 24th International Conference on World Wide Web","first-page":"1067","article-title":"LINE: large-scale information network embedding","author":"Tang","year":"2015"},{"key":"10.1016\/j.knosys.2025.114747_bib0035","series-title":"Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","first-page":"855","article-title":"node2vec: scalable feature learning for networks","author":"Grover","year":"2016"},{"key":"10.1016\/j.knosys.2025.114747_bib0036","unstructured":"P. Veli\u010dkovi\u0107, G. Cucurull, A. Casanova, A. Romero, P. Li\u00f2, Y. Bengio, Graph attention networks, (2017). arXiv preprint arxiv:1710.10903."},{"issue":"5","key":"10.1016\/j.knosys.2025.114747_bib0037","doi-asserted-by":"crossref","first-page":"1169","DOI":"10.1109\/TBDATA.2020.3019478","article-title":"Multi-label graph convolutional network representation learning","volume":"8","author":"Shi","year":"2022","journal-title":"IEEE Trans. Big Data"},{"key":"10.1016\/j.knosys.2025.114747_bib0038","series-title":"Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","first-page":"135","article-title":"metapath2vec: scalable representation learning for heterogeneous networks","author":"Dong","year":"2017"},{"key":"10.1016\/j.knosys.2025.114747_bib0039","series-title":"Proceedings of the 2017 ACM on Conference on Information and Knowledge Management","first-page":"1797","article-title":"HIN2Vec: explore meta-paths in heterogeneous information networks for representation learning","author":"Fu","year":"2017"},{"issue":"2","key":"10.1016\/j.knosys.2025.114747_bib0040","doi-asserted-by":"crossref","first-page":"1157","DOI":"10.1002\/int.22664","article-title":"AHNA: adaptive representation learning for attributed heterogeneous networks","volume":"37","author":"Shu","year":"2022","journal-title":"Int. J. Intell. Syst."},{"key":"10.1016\/j.knosys.2025.114747_bib0041","doi-asserted-by":"crossref","first-page":"127397","DOI":"10.1109\/ACCESS.2021.3110200","article-title":"ATTRHIN: network representation learning method for heterogeneous information network","volume":"9","author":"Zhou","year":"2021","journal-title":"IEEE Access"},{"issue":"6","key":"10.1016\/j.knosys.2025.114747_bib0042","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1109\/MIS.2020.3027677","article-title":"Learning embeddings based on global structural similarity in heterogeneous networks","volume":"36","author":"Wen","year":"2020","journal-title":"IEEE Intell. Syst."},{"key":"10.1016\/j.knosys.2025.114747_bib0043","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2021.116031","article-title":"MBRep: motif-based representation learning in heterogeneous networks","volume":"190","author":"Hu","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.knosys.2025.114747_bib0044","series-title":"Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","first-page":"1177","article-title":"PME: projected metric embedding on heterogeneous networks for link prediction","author":"Chen","year":"2018"},{"key":"10.1016\/j.knosys.2025.114747_bib0045","series-title":"Proceedings of the Tenth ACM International Conference on Web Search and Data Mining","first-page":"741","article-title":"Embedding of embedding (EOE) joint embedding for coupled heterogeneous networks","author":"Xu","year":"2017"},{"key":"10.1016\/j.knosys.2025.114747_bib0046","series-title":"Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","first-page":"1165","article-title":"PTE: predictive text embedding through large-scale heterogeneous text networks","author":"Tang","year":"2015"},{"key":"10.1016\/j.knosys.2025.114747_bib0047","series-title":"Proceedings of the World Wide Web Conference","first-page":"2022","article-title":"Heterogeneous graph attention network","author":"Wang","year":"2019"},{"key":"10.1016\/j.knosys.2025.114747_bib0048","series-title":"Proceedings of the Web Conference","first-page":"2331","article-title":"MAGNN: metapath aggregated graph neural network for heterogeneous graph embedding","author":"Fu","year":"2020"},{"key":"10.1016\/j.knosys.2025.114747_bib0049","series-title":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","first-page":"793","article-title":"Heterogeneous graph neural network","author":"Zhang","year":"2019"},{"key":"10.1016\/j.knosys.2025.114747_bib0050","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"4132","article-title":"An attention-based graph neural network for heterogeneous structural learning","author":"Hong","year":"2020"},{"key":"10.1016\/j.knosys.2025.114747_bib0051","series-title":"Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","first-page":"1161","article-title":"HGCN: a heterogeneous graph convolutional network-based deep learning model toward collective classification","author":"Zhu","year":"2020"},{"key":"10.1016\/j.knosys.2025.114747_bib0052","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"4697","article-title":"Heterogeneous graph structure learning for graph neural networks","author":"Zhao","year":"2021"},{"issue":"21","key":"10.1016\/j.knosys.2025.114747_bib0053","doi-asserted-by":"crossref","first-page":"25626","DOI":"10.1007\/s10489-023-04840-w","article-title":"An interlayer feature fusion-based heterogeneous graph neural network","volume":"53","author":"Feng","year":"2023","journal-title":"Appl. Intell."},{"key":"10.1016\/j.knosys.2025.114747_bib0054","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.120810","article-title":"Semantic-guided graph neural network for heterogeneous graph embedding","volume":"232","author":"Han","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.knosys.2025.114747_bib0055","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2025.129763","article-title":"Deep attributed network representation learning via enhanced local attribute neighbor","volume":"631","author":"Han","year":"2025","journal-title":"Neurocomputing"},{"key":"10.1016\/j.knosys.2025.114747_bib0056","series-title":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","first-page":"1358","article-title":"Representation learning for attributed multiplex heterogeneous network","author":"Cen","year":"2019"},{"key":"10.1016\/j.knosys.2025.114747_bib0057","doi-asserted-by":"crossref","first-page":"98490","DOI":"10.1109\/ACCESS.2022.3187088","article-title":"Siamese network based multiscale self-supervised heterogeneous graph representation learning","volume":"10","author":"Chen","year":"2022","journal-title":"IEEE Access"},{"key":"10.1016\/j.knosys.2025.114747_bib0058","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.future.2023.05.026","article-title":"An effective heterogeneous information network representation learning framework","volume":"148","author":"Han","year":"2023","journal-title":"Future Gener. Comput. Syst."},{"issue":"2","key":"10.1016\/j.knosys.2025.114747_bib0059","first-page":"1637","article-title":"Interpretable and efficient heterogeneous graph convolutional network","volume":"35","author":"Yang","year":"2023","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"17","key":"10.1016\/j.knosys.2025.114747_bib0060","doi-asserted-by":"crossref","first-page":"8073","DOI":"10.1007\/s10489-024-05567-y","article-title":"MHGNN: multi-view fusion based heterogeneous graph neural network","volume":"54","author":"Li","year":"2024","journal-title":"Appl. Intell."}],"container-title":["Knowledge-Based Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S095070512501785X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S095070512501785X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T21:46:41Z","timestamp":1773265601000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S095070512501785X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11]]},"references-count":60,"alternative-id":["S095070512501785X"],"URL":"https:\/\/doi.org\/10.1016\/j.knosys.2025.114747","relation":{},"ISSN":["0950-7051"],"issn-type":[{"value":"0950-7051","type":"print"}],"subject":[],"published":{"date-parts":[[2025,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Heterogeneous network representation learning via multiple attention mechanisms","name":"articletitle","label":"Article Title"},{"value":"Knowledge-Based Systems","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.knosys.2025.114747","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 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":"114747"}}