{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T00:02:17Z","timestamp":1784678537724,"version":"3.55.0"},"reference-count":57,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100004772","name":"Natural Science Foundation of Ningxia Province","doi-asserted-by":"publisher","award":["2024AAC05011"],"award-info":[{"award-number":["2024AAC05011"]}],"id":[{"id":"10.13039\/501100004772","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62306157"],"award-info":[{"award-number":["62306157"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100016692","name":"Key Research and Development Program of Ningxia","doi-asserted-by":"publisher","award":["2025BEH04048"],"award-info":[{"award-number":["2025BEH04048"]}],"id":[{"id":"10.13039\/100016692","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":[[2026,3]]},"DOI":"10.1016\/j.knosys.2026.115385","type":"journal-article","created":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T09:58:44Z","timestamp":1769075924000},"page":"115385","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":2,"special_numbering":"C","title":["Enhancing Heterogeneous Graph Learning with Semantic-Aware Meta-Path Diffusion and Dual Optimization"],"prefix":"10.1016","volume":"337","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-1644-0333","authenticated-orcid":false,"given":"Guanghua","family":"Ding","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3112-4861","authenticated-orcid":false,"given":"Rui","family":"Tang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1249-9190","authenticated-orcid":false,"given":"Xian","family":"Mo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.knosys.2026.115385_bib0001","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.2026.115385_bib0002","series-title":"The World Wide Web Conference","first-page":"2022","article-title":"Heterogeneous graph attention network","author":"Wang","year":"2019"},{"key":"10.1016\/j.knosys.2026.115385_bib0003","series-title":"Proceedings of the 28th ACM SIGKDD conference on knowledge discovery and data mining","first-page":"2377","article-title":"Multiplex heterogeneous graph convolutional network","author":"Yu","year":"2022"},{"key":"10.1016\/j.knosys.2026.115385_bib0004","series-title":"Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining","first-page":"1726","article-title":"Self-supervised heterogeneous graph neural network with co-contrastive learning","author":"Wang","year":"2021"},{"key":"10.1016\/j.knosys.2026.115385_bib0005","series-title":"Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining","first-page":"2478","article-title":"Metapath-guided heterogeneous graph neural network for intent recommendation","author":"Fan","year":"2019"},{"key":"10.1016\/j.knosys.2026.115385_bib0006","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2025.113008","article-title":"Attention-aware graph contrastive learning with topological relationship for recommendation","volume":"174","author":"Mo","year":"2025","journal-title":"Applied Soft Computing"},{"key":"10.1016\/j.knosys.2026.115385_bib0007","series-title":"Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining","first-page":"1531","article-title":"Leveraging meta-path based context for top-n recommendation with a neural co-attention model","author":"Hu","year":"2018"},{"key":"10.1016\/j.knosys.2026.115385_bib0008","series-title":"Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining","first-page":"1150","article-title":"Are we really making much progress? revisiting, benchmarking and refining heterogeneous graph neural networks","author":"Lv","year":"2021"},{"key":"10.1016\/j.knosys.2026.115385_bib0009","series-title":"Proceedings of the Web Conference 2020","first-page":"2331","article-title":"Magnn: Metapath aggregated graph neural network for heterogeneous graph embedding","author":"Fu","year":"2020"},{"key":"10.1016\/j.knosys.2026.115385_bib0010","article-title":"Hagnn: Hybrid aggregation for heterogeneous graph neural networks","author":"Zhu","year":"2024","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"10.1016\/j.knosys.2026.115385_bib0011","series-title":"Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining","first-page":"793","article-title":"Heterogeneous graph neural network","author":"Zhang","year":"2019"},{"key":"10.1016\/j.knosys.2026.115385_bib0012","unstructured":"S. Sang, K.-J. Chen, et al., HeGMN: Heterogeneous Graph Matching Network for Learning Graph Similarity, (2025). arXiv: 2503.08739."},{"key":"10.1016\/j.knosys.2026.115385_bib0013","series-title":"Proceedings of the AAAI conference on artificial intelligence","first-page":"10816","article-title":"Simple and efficient heterogeneous graph neural network","volume":"37","author":"Yang","year":"2023"},{"key":"10.1016\/j.knosys.2026.115385_bib0014","series-title":"Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining","first-page":"793","article-title":"Heterogeneous graph neural network","author":"Zhang","year":"2019"},{"key":"10.1016\/j.knosys.2026.115385_bib0015","unstructured":"P. Veli\u010dkovi\u0107, G. Cucurull, A. Casanova, A. Romero, P. Lio, Y. Bengio, Graph attention networks, (2017). arXiv: 1710.10903."},{"key":"10.1016\/j.knosys.2026.115385_bib0016","series-title":"Proceedings of the AAAI conference on artificial intelligence","first-page":"5892","article-title":"Multi-stage self-supervised learning for graph convolutional networks on graphs with few labeled nodes","volume":"34","author":"Sun","year":"2020"},{"issue":"1","key":"10.1016\/j.knosys.2026.115385_bib0017","doi-asserted-by":"crossref","first-page":"1003","DOI":"10.1109\/TETCI.2023.3322341","article-title":"Heterogeneous graph contrastive learning with metapath-based augmentations","volume":"8","author":"Chen","year":"2023","journal-title":"IEEE Transactions on Emerging Topics in Computational Intelligence"},{"key":"10.1016\/j.knosys.2026.115385_bib0018","doi-asserted-by":"crossref","DOI":"10.1016\/j.neunet.2024.106645","article-title":"GTC: GNN-transformer co-contrastive learning for self-supervised heterogeneous graph representation","volume":"181","author":"Sun","year":"2025","journal-title":"Neural Networks"},{"key":"10.1016\/j.knosys.2026.115385_bib0019","series-title":"Proceedings of the Eighteenth ACM International Conference on Web Search and Data Mining","first-page":"213","article-title":"Self-supervised Time-aware Heterogeneous Hypergraph Learning for Dynamic Graph-level Classification","author":"Hayat","year":"2025"},{"key":"10.1016\/j.knosys.2026.115385_bib0020","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"16399","article-title":"Contrastive Auxiliary Learning with Structure Transformation for Heterogeneous Graphs","volume":"39","author":"Du","year":"2025"},{"key":"10.1016\/j.knosys.2026.115385_bib0021","series-title":"Proceedings of the 2023 SIAM international conference on data mining (SDM)","first-page":"136","article-title":"Heterogeneous graph contrastive multi-view learning","author":"Wang","year":"2023"},{"key":"10.1016\/j.knosys.2026.115385_bib0022","series-title":"Proceedings of the 31st ACM international conference on information & knowledge management","first-page":"894","article-title":"X-GOAL: Multiplex heterogeneous graph prototypical contrastive learning","author":"Jing","year":"2022"},{"issue":"11","key":"10.1016\/j.knosys.2026.115385_bib0023","doi-asserted-by":"crossref","first-page":"992","DOI":"10.14778\/3402707.3402736","article-title":"Pathsim: Meta path-based top-k similarity search in heterogeneous information networks","volume":"4","author":"Sun","year":"2011","journal-title":"Proceedings of the VLDB Endowment"},{"key":"10.1016\/j.knosys.2026.115385_bib0024","doi-asserted-by":"crossref","DOI":"10.1016\/j.neunet.2024.106645","article-title":"GTC: GNN-transformer co-contrastive learning for self-supervised heterogeneous graph representation","volume":"181","author":"Sun","year":"2025","journal-title":"Neural Networks"},{"key":"10.1016\/j.knosys.2026.115385_bib0025","series-title":"The world wide web conference","first-page":"2022","article-title":"Heterogeneous graph attention network","author":"Wang","year":"2019"},{"key":"10.1016\/j.knosys.2026.115385_bib0026","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","volume":"34","author":"Hong","year":"2020"},{"key":"10.1016\/j.knosys.2026.115385_bib0027","series-title":"Proceedings of the web conference 2020","first-page":"2704","article-title":"Heterogeneous graph transformer","author":"Hu","year":"2020"},{"key":"10.1016\/j.knosys.2026.115385_bib0028","article-title":"Graph transformer networks","volume":"32","author":"Yun","year":"2019","journal-title":"Advances in neural information processing systems"},{"key":"10.1016\/j.knosys.2026.115385_bib0029","series-title":"2019 IEEE international conference on data mining (ICDM)","first-page":"1534","article-title":"Relation structure-aware heterogeneous graph neural network","author":"Zhu","year":"2019"},{"key":"10.1016\/j.knosys.2026.115385_bib0030","series-title":"Proceedings of the 28th ACM SIGKDD conference on knowledge discovery and data mining","first-page":"2377","article-title":"Multiplex heterogeneous graph convolutional network","author":"Yu","year":"2022"},{"key":"10.1016\/j.knosys.2026.115385_bib0031","series-title":"Proceedings of the 29th ACM SIGKDD conference on knowledge discovery and data mining","first-page":"482","article-title":"Multiplex heterogeneous graph neural network with behavior pattern modeling","author":"Fu","year":"2023"},{"key":"10.1016\/j.knosys.2026.115385_bib0032","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.127293","article-title":"DAG-HFC: Dual-domain attention and graph optimization network for heterogeneous graph feature completion","volume":"278","author":"Jiang","year":"2025","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.knosys.2026.115385_bib0033","article-title":"AdaGCL+: An Adaptive Subgraph Contrastive Learning Towards Tackling Topological Bias","author":"Wang","year":"2025","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"10.1016\/j.knosys.2026.115385_bib0034","series-title":"Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","first-page":"2640","article-title":"Optimizing ood detection in molecular graphs: A novel approach with diffusion models","author":"Shen","year":"2024"},{"key":"10.1016\/j.knosys.2026.115385_bib0035","series-title":"International Conference on Learning Representations","article-title":"Deep graph infomax","author":"Veli","year":"2019"},{"key":"10.1016\/j.knosys.2026.115385_bib0036","unstructured":"Y. Zhu, Y. Xu, F. Yu, Q. Liu, S. Wu, L. Wang, Deep graph contrastive representation learning, (2020). arXiv: 2006.04131."},{"key":"10.1016\/j.knosys.2026.115385_bib0037","series-title":"Proceedings of the web conference 2021","first-page":"2069","article-title":"Graph contrastive learning with adaptive augmentation","author":"Zhu","year":"2021"},{"key":"10.1016\/j.knosys.2026.115385_bib0038","series-title":"Proceedings of the 2022 SIAM International Conference on Data Mining (SDM)","first-page":"82","article-title":"Structure-enhanced heterogeneous graph contrastive learning","author":"Zhu","year":"2022"},{"key":"10.1016\/j.knosys.2026.115385_bib0039","series-title":"Proceedings of the 30th ACM international conference on information & knowledge management","first-page":"803","article-title":"Contrastive pre-training of GNNs on heterogeneous graphs","author":"Jiang","year":"2021"},{"key":"10.1016\/j.knosys.2026.115385_bib0040","series-title":"Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining","first-page":"1726","article-title":"Self-supervised heterogeneous graph neural network with co-contrastive learning","author":"Wang","year":"2021"},{"key":"10.1016\/j.knosys.2026.115385_bib0041","series-title":"Proceedings of the 2023 SIAM international conference on data mining (SDM)","first-page":"136","article-title":"Heterogeneous graph contrastive multi-view learning","author":"Wang","year":"2023"},{"key":"10.1016\/j.knosys.2026.115385_bib0042","series-title":"Proceedings of the AAAI conference on artificial intelligence","first-page":"5371","article-title":"Unsupervised attributed multiplex network embedding","volume":"34","author":"Park","year":"2020"},{"key":"10.1016\/j.knosys.2026.115385_bib0043","series-title":"Proceedings of the ACM web conference 2022","first-page":"1631","article-title":"Collaborative knowledge distillation for heterogeneous information network embedding","author":"Wang","year":"2022"},{"issue":"4","key":"10.1016\/j.knosys.2026.115385_bib0044","doi-asserted-by":"crossref","first-page":"6858","DOI":"10.1109\/TNNLS.2024.3388424","article-title":"Select your own counterparts: Self-supervised graph contrastive learning with positive sampling","volume":"36","author":"Wang","year":"2024","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"10.1016\/j.knosys.2026.115385_bib0045","doi-asserted-by":"crossref","first-page":"107403","DOI":"10.52202\/079017-3412","article-title":"Gft: Graph foundation model with transferable tree vocabulary","volume":"37","author":"Wang","year":"2024","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.knosys.2026.115385_bib0046","unstructured":"Z. Wang, Z. Zhang, T. Ma, N.V. Chawla, C. Zhang, Y. Ye, Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees, (2024c). arXiv: 2412.16441."},{"key":"10.1016\/j.knosys.2026.115385_bib0047","series-title":"The Thirty-ninth Annual Conference on Neural Information Processing Systems","article-title":"Generative Graph Pattern Machine","author":"Wang","year":"2025"},{"issue":"11","key":"10.1016\/j.knosys.2026.115385_bib0048","doi-asserted-by":"crossref","first-page":"992","DOI":"10.14778\/3402707.3402736","article-title":"Pathsim: Meta path-based top-k similarity search in heterogeneous information networks","volume":"4","author":"Sun","year":"2011","journal-title":"Proceedings of the VLDB Endowment"},{"issue":"20","key":"10.1016\/j.knosys.2026.115385_bib0049","first-page":"10","article-title":"Graph attention networks","volume":"1050","author":"Velickovic","year":"2017","journal-title":"stat"},{"key":"10.1016\/j.knosys.2026.115385_bib0050","series-title":"The World Wide Web Conference","first-page":"2022","article-title":"Heterogeneous graph attention network","author":"Wang","year":"2019"},{"key":"10.1016\/j.knosys.2026.115385_bib0051","unstructured":"T.N. Kipf, Semi-supervised classification with graph convolutional networks, (2016). arXiv: 1609.02907."},{"key":"10.1016\/j.knosys.2026.115385_bib0052","doi-asserted-by":"crossref","DOI":"10.1109\/TKDE.2024.3434956","article-title":"Efficient heterogeneous graph learning via random projection","author":"Hu","year":"2024","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"10.1016\/j.knosys.2026.115385_bib0053","series-title":"Proceedings of the Eighteenth ACM International Conference on Web Search and Data Mining","first-page":"40","article-title":"DiffGraph: Heterogeneous Graph Diffusion Model","author":"Li","year":"2025"},{"key":"10.1016\/j.knosys.2026.115385_bib0054","first-page":"5812","article-title":"Graph contrastive learning with augmentations","volume":"33","author":"You","year":"2020","journal-title":"Advances in neural information processing systems"},{"issue":"2","key":"10.1016\/j.knosys.2026.115385_bib0055","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1109\/TIT.1982.1056489","article-title":"Least squares quantization in PCM","volume":"28","author":"Lloyd","year":"1982","journal-title":"IEEE transactions on information theory"},{"key":"10.1016\/j.knosys.2026.115385_bib0056","series-title":"Proceedings of the 2022 SIAM International Conference on Data Mining (SDM)","first-page":"82","article-title":"Structure-enhanced heterogeneous graph contrastive learning","author":"Zhu","year":"2022"},{"issue":"Nov","key":"10.1016\/j.knosys.2026.115385_bib0057","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"van der","year":"2008","journal-title":"Journal of machine learning research"}],"container-title":["Knowledge-Based Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705126001280?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705126001280?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T23:24:33Z","timestamp":1784676273000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0950705126001280"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3]]},"references-count":57,"alternative-id":["S0950705126001280"],"URL":"https:\/\/doi.org\/10.1016\/j.knosys.2026.115385","relation":{},"ISSN":["0950-7051"],"issn-type":[{"value":"0950-7051","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Enhancing Heterogeneous Graph Learning with Semantic-Aware Meta-Path Diffusion and Dual Optimization","name":"articletitle","label":"Article Title"},{"value":"Knowledge-Based Systems","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.knosys.2026.115385","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":"115385"}}