{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T14:55:34Z","timestamp":1777733734089,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":49,"publisher":"ACM","license":[{"start":{"date-parts":[[2025,3,10]],"date-time":"2025-03-10T00:00:00Z","timestamp":1741564800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Australian Research Council (ARC)","award":["LP210301259, DP230100899"],"award-info":[{"award-number":["LP210301259, DP230100899"]}]},{"name":"MQ Research Acceleration Project (MQRAS)","award":["334481525"],"award-info":[{"award-number":["334481525"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,3,10]]},"DOI":"10.1145\/3701551.3703504","type":"proceedings-article","created":{"date-parts":[[2025,2,26]],"date-time":"2025-02-26T12:33:36Z","timestamp":1740573216000},"page":"213-221","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Self-supervised Time-aware Heterogeneous Hypergraph Learning for Dynamic Graph-level Classification"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8177-2042","authenticated-orcid":false,"given":"Malik Khizar","family":"Hayat","sequence":"first","affiliation":[{"name":"Macquarie University, Sydney, NSW, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9123-5133","authenticated-orcid":false,"given":"Shan","family":"Xue","sequence":"additional","affiliation":[{"name":"Macquarie University, Sydney, NSW, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1371-5801","authenticated-orcid":false,"given":"Jia","family":"Wu","sequence":"additional","affiliation":[{"name":"Macquarie University, Sydney, NSW, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7426-0292","authenticated-orcid":false,"given":"Bilal","family":"Khan","sequence":"additional","affiliation":[{"name":"Macquarie University, Sydney, NSW, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4408-1952","authenticated-orcid":false,"given":"Jian","family":"Yang","sequence":"additional","affiliation":[{"name":"Macquarie University, Sydney, NSW, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,3,10]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-93037-4_14"},{"key":"e_1_3_2_1_2_1","volume-title":"Unsupervised inductive graph-level representation learning via graph-graph proximity. arXiv preprint arXiv:1904.01098","author":"Bai Yunsheng","year":"2019","unstructured":"Yunsheng Bai, Hao Ding, Yang Qiao, Agustin Marinovic, Ken Gu, Ting Chen, Yizhou Sun, and Wei Wang. 2019. Unsupervised inductive graph-level representation learning via graph-graph proximity. arXiv preprint arXiv:1904.01098 (2019)."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/3583780.3614899"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3411953"},{"key":"e_1_3_2_1_5_1","volume-title":"A graph distance metric based on the maximal common subgraph. Pattern recognition letters","author":"Bunke Horst","year":"1998","unstructured":"Horst Bunke and Kim Shearer. 1998. A graph distance metric based on the maximal common subgraph. Pattern recognition letters, Vol. 19, 3--4 (1998), 255--259."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2023.10.040"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098036"},{"key":"e_1_3_2_1_8_1","volume-title":"Self-supervised Hypergraph Representation Learning. In 2022 IEEE Intl. Conf. on Big Data (Big Data). IEEE, 505--514","author":"Du Boxin","year":"2022","unstructured":"Boxin Du, Changhe Yuan, Robert Barton, Tal Neiman, and Hanghang Tong. 2022. Self-supervised Hypergraph Representation Learning. In 2022 IEEE Intl. Conf. on Big Data (Big Data). IEEE, 505--514."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33013558"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380297"},{"key":"e_1_3_2_1_11_1","volume-title":"General Hypergraph Neural Networks","author":"Gao Yue","year":"2022","unstructured":"Yue Gao, Yifan Feng, Shuyi Ji, and Rongrong Ji. 2022. HGNN$^$: General Hypergraph Neural Networks. IEEE Trans. on Pattern Analysis and Machine Intelligence (2022)."},{"key":"e_1_3_2_1_12_1","volume-title":"Proceedings of the thirteenth Intl. Conf. on Artificial Intelligence and Statistics. JMLR Workshop and Conference Proceedings, 249--256","author":"Glorot Xavier","year":"2010","unstructured":"Xavier Glorot and Yoshua Bengio. 2010. Understanding the difficulty of training deep feedforward neural networks. In Proceedings of the thirteenth Intl. Conf. on Artificial Intelligence and Statistics. JMLR Workshop and Conference Proceedings, 249--256."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2019.06.024"},{"key":"e_1_3_2_1_14_1","volume-title":"Dyngem: Deep embedding method for dynamic graphs. arXiv preprint arXiv:1805.11273","author":"Goyal Palash","year":"2018","unstructured":"Palash Goyal, Nitin Kamra, Xinran He, and Yan Liu. 2018. Dyngem: Deep embedding method for dynamic graphs. arXiv preprint arXiv:1805.11273 (2018)."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939754"},{"key":"e_1_3_2_1_16_1","volume-title":"Advances in Neural Information Processing Systems","volume":"30","author":"Hamilton Will","year":"2017","unstructured":"Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. Advances in Neural Information Processing Systems, Vol. 30 (2017)."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/TAI.2024.3450658"},{"key":"e_1_3_2_1_18_1","volume-title":"Self-supervised Heterogeneous Hypergraph Learning with Context-aware Pooling for Graph-level Classification. In 2023 IEEE Intl. Conf. on Data Mining (ICDM). IEEE, 140--149","author":"Hayat Malik Khizar","year":"2023","unstructured":"Malik Khizar Hayat, Shan Xue, and Jian Yang. 2023. Self-supervised Heterogeneous Hypergraph Learning with Context-aware Pooling for Graph-level Classification. In 2023 IEEE Intl. Conf. on Data Mining (ICDM). IEEE, 140--149."},{"key":"e_1_3_2_1_19_1","volume-title":"Self-supervised multi-hop heterogeneous hypergraph embedding with informative pooling for graph-level classification. Knowledge and Information Systems","author":"Hayat Malik Khizar","year":"2024","unstructured":"Malik Khizar Hayat, Shan Xue, and Jian Yang. 2024a. Self-supervised multi-hop heterogeneous hypergraph embedding with informative pooling for graph-level classification. Knowledge and Information Systems (2024), 1--24."},{"key":"e_1_3_2_1_20_1","volume-title":"NeurIPS 2022 Temporal Graph Learning Workshop.","author":"Huang Chenghan","year":"2022","unstructured":"Chenghan Huang, Lili Wang, Xinyuan Cao, Weicheng Ma, and Soroush Vosoughi. 2022. Learning dynamic graph embeddings using random walk with temporal backtracking. In NeurIPS 2022 Temporal Graph Learning Workshop."},{"key":"e_1_3_2_1_21_1","first-page":"4314","article-title":"Learning dynamic graph representation of brain connectome with spatio-temporal attention","volume":"34","author":"Kim Byung-Hoon","year":"2021","unstructured":"Byung-Hoon Kim, Jong Chul Ye, and Jae-Jin Kim. 2021. Learning dynamic graph representation of brain connectome with spatio-temporal attention. Advances in Neural Information Processing Systems, Vol. 34 (2021), 4314--4327.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_22_1","unstructured":"Thomas N Kipf and Max Welling. 2016. Variational graph auto-encoders. (2016)."},{"key":"e_1_3_2_1_23_1","volume-title":"Intl. Conf. on machine learning. pmlr, 3734--3743","author":"Lee Junhyun","year":"2019","unstructured":"Junhyun Lee, Inyeop Lee, and Jaewoo Kang. 2019. Self-attention graph pooling. In Intl. Conf. on machine learning. pmlr, 3734--3743."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBDATA.2023.3275374"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2023.126441"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449821"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP48485.2024.10446248"},{"key":"e_1_3_2_1_28_1","volume-title":"Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101","author":"Loshchilov Ilya","year":"2017","unstructured":"Ilya Loshchilov and Frank Hutter. 2017. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101 (2017)."},{"key":"e_1_3_2_1_29_1","volume-title":"graph2vec: Learning distributed representations of graphs. arXiv preprint arXiv:1707.05005","author":"Narayanan Annamalai","year":"2017","unstructured":"Annamalai Narayanan, Mahinthan Chandramohan, Rajasekar Venkatesan, Lihui Chen, Yang Liu, and Shantanu Jaiswal. 2017. graph2vec: Learning distributed representations of graphs. arXiv preprint arXiv:1707.05005 (2017)."},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5984"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403168"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i7.20787"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2019.101663"},{"key":"e_1_3_2_1_34_1","volume-title":"Hegcl: Advance self-supervised learning in heterogeneous graph-level representation","author":"Shi Gen","year":"2023","unstructured":"Gen Shi, Yifan Zhu, Jian K Liu, and Xuesong Li. 2023. Hegcl: Advance self-supervised learning in heterogeneous graph-level representation. IEEE Trans. on Neural Networks and Learning Systems (2023)."},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449822"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2023.04.004"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482389"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939753"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467415"},{"key":"e_1_3_2_1_40_1","volume-title":"Dual subgraph-based graph neural network for friendship prediction in location-based social networks. ACM Trans. on Knowledge Discovery from Data","author":"Wei Xuemei","year":"2023","unstructured":"Xuemei Wei, Yezheng Liu, Jianshan Sun, Yuanchun Jiang, Qifeng Tang, and Kun Yuan. 2023. Dual subgraph-based graph neural network for friendship prediction in location-based social networks. ACM Trans. on Knowledge Discovery from Data, Vol. 17, 3 (2023), 1--28."},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512164"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539473"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2019.2957097"},{"key":"e_1_3_2_1_44_1","first-page":"30414","article-title":"Infogcl: Information-aware graph contrastive learning","volume":"34","author":"Xu Dongkuan","year":"2021","unstructured":"Dongkuan Xu, Wei Cheng, Dongsheng Luo, Haifeng Chen, and Xiang Zhang. 2021. Infogcl: Information-aware graph contrastive learning. Advances in Neural Information Processing Systems, Vol. 34 (2021), 30414--30425.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_45_1","first-page":"5812","article-title":"Graph contrastive learning with augmentations","volume":"33","author":"You Yuning","year":"2020","unstructured":"Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, and Yang Shen. 2020. Graph contrastive learning with augmentations. Advances in Neural Information Processing Systems, Vol. 33 (2020), 5812--5823.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/3583780.3615081"},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1145\/3583780.3615023"},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1038\/s43588-023-00558-4"},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11257"}],"event":{"name":"WSDM '25: The Eighteenth ACM International Conference on Web Search and Data Mining","location":"Hannover Germany","acronym":"WSDM '25","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data","SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the Eighteenth ACM International Conference on Web Search and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3701551.3703504","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3701551.3703504","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T09:16:23Z","timestamp":1755767783000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3701551.3703504"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,10]]},"references-count":49,"alternative-id":["10.1145\/3701551.3703504","10.1145\/3701551"],"URL":"https:\/\/doi.org\/10.1145\/3701551.3703504","relation":{},"subject":[],"published":{"date-parts":[[2025,3,10]]},"assertion":[{"value":"2025-03-10","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}