{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T02:20:07Z","timestamp":1777342807370,"version":"3.51.4"},"reference-count":49,"publisher":"Association for Computing Machinery (ACM)","issue":"5","license":[{"start":{"date-parts":[[2024,4,29]],"date-time":"2024-04-29T00:00:00Z","timestamp":1714348800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2018YFB0505000"],"award-info":[{"award-number":["2018YFB0505000"]}]},{"name":"Donghai Laboratory","award":["DH-2022ZY0013"],"award-info":[{"award-number":["DH-2022ZY0013"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Inf. Syst."],"published-print":{"date-parts":[[2024,9,30]]},"abstract":"<jats:p>Event prediction is a vital and challenging task in temporal knowledge graphs (TKGs), which have played crucial roles in various applications. Recently, many graph neural networks based approaches are proposed to model the graph structure information in TKGs. However, these approaches only construct graphs based on quadruplets and model the pairwise correlation between entities, which fail to capture the high-order correlations among entities. To this end, we propose DHyper, a recurrent Dual Hypergraph neural network for event prediction in TKGs, which simultaneously models the influences of the high-order correlations among both entities and relations. Specifically, a dual hypergraph learning module is proposed to discover the high-order correlations among entities and among relations in a parameterized way. A dual hypergraph message passing network is introduced to perform the information aggregation and representation fusion on the entity hypergraph and the relation hypergraph. Extensive experiments on six real-world datasets demonstrate that DHyper achieves the state-of-the-art performances, outperforming the best baseline by an average of 13.09%, 4.26%, 17.60%, and 18.03% in MRR, Hits@1, Hits@3, and Hits@10, respectively.<\/jats:p>","DOI":"10.1145\/3653015","type":"journal-article","created":{"date-parts":[[2024,3,18]],"date-time":"2024-03-18T14:27:50Z","timestamp":1710772070000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":11,"title":["DHyper: A Recurrent Dual Hypergraph Neural Network for Event Prediction in Temporal Knowledge Graphs"],"prefix":"10.1145","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0265-2056","authenticated-orcid":false,"given":"Xing","family":"Tang","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, Zhejiang University, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1934-5992","authenticated-orcid":false,"given":"Ling","family":"Chen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Blockchain and Data Security, College of Computer Science and Technology, Zhejiang University, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8625-7135","authenticated-orcid":false,"given":"Hongyu","family":"Shi","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Zhejiang University, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9285-6970","authenticated-orcid":false,"given":"Dandan","family":"Lyu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Zhejiang University, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,4,29]]},"reference":[{"issue":"2","key":"e_1_3_2_2_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3531267","article-title":"Time-aware path reasoning on knowledge graph for recommendation","volume":"41","author":"Zhao Yuyue","year":"2022","unstructured":"Yuyue Zhao, Xiang Wang, Jiawei Chen, Yashen Wang, Wei Tang, Xiangnan He, and Haiyong Xie. 2022. Time-aware path reasoning on knowledge graph for recommendation. ACM Transactions on Information Systems 41, 2 (2022), 1\u201326.","journal-title":"ACM Transactions on Information Systems"},{"issue":"3","key":"e_1_3_2_3_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3576921","article-title":"Preference-aware graph attention networks for cross-domain recommendations with collaborative knowledge graph","volume":"41","author":"Li Yakun","year":"2023","unstructured":"Yakun Li, Lei Hou, and Juanzi Li. 2023. Preference-aware graph attention networks for cross-domain recommendations with collaborative knowledge graph. ACM Transactions on Information Systems 41, 3 (2023), 1\u201326.","journal-title":"ACM Transactions on Information Systems"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1145\/3477051"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D18-1225"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1145\/3407190"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403209"},{"issue":"12","key":"e_1_3_2_8_2","first-page":"2734","article-title":"Optimizing DNN computation graph using graph substitutions","volume":"13","author":"Fang Jingzhi","year":"2020","unstructured":"Jingzhi Fang, Yanyan Shen, Yue Wang, and Lei Chen. 2020. Optimizing DNN computation graph using graph substitutions. In VLDB, 13, 12 (2020), 2734\u20132746.","journal-title":"VLDB"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33013558"},{"issue":"3","key":"e_1_3_2_10_2","doi-asserted-by":"crossref","first-page":"3181","DOI":"10.1109\/TPAMI.2022.3182052","article-title":"HGNN+: General hypergraph neural networks","volume":"45","author":"Gao Yue","year":"2022","unstructured":"Yue Gao, Yifan Feng, Shuyi Ji, and Rongrong Ji. 2022. HGNN+: General hypergraph neural networks. IEEE Transactions on Pattern Analysis and Machine Intelligence 45, 3 (2022), 3181\u20133199.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.emnlp-main.541"},{"key":"e_1_3_2_12_2","first-page":"1","volume-title":"ICLR","author":"Kingma Diederik P.","year":"2015","unstructured":"Diederik P. Kingma and Jimmy Ba. 2015. Adam: A method for stochastic optimization. In ICLR. 1\u201315."},{"key":"e_1_3_2_13_2","first-page":"1771","volume-title":"WWW","author":"Leblay Julien","year":"2018","unstructured":"Julien Leblay and Melisachew Wudage Chekol. 2018. Deriving validity time in knowledge graph. In WWW. 1771\u20131776."},{"issue":"4","key":"e_1_3_2_14_2","first-page":"1","article-title":"GDELT: Global data on events, location, and tone, 1979\u20132012","volume":"2","year":"2013","unstructured":"Kalev Leetaru and Philip A. Schrodt. 2013. GDELT: Global data on events, location, and tone, 1979\u20132012. ISA Annual Convention 2, 4 (2013), 1\u201349.","journal-title":"ISA Annual Convention"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462963"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2022\/299"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1145\/3511808.3557280"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-21743-2_54"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-93417-4_38"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.emnlp-main.655"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2019.01.005"},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","unstructured":"Xing Tang and Ling Chen. 2023. GTRL: An entity group-aware temporal knowledge graph representation learning method. IEEE Transactions on Knowledge and Data Engineering 99 (2023) 1--16. DOI:10.1109\/TKDE.2023.3334165","DOI":"10.1109\/TKDE.2023.3334165"},{"key":"e_1_3_2_23_2","first-page":"3462","volume-title":"ICML","author":"Trivedi Rakshit","year":"2017","unstructured":"Rakshit Trivedi, Hanjun Dai, Yichen Wang, and Le Song. 2017. Know-Evolve: Deep temporal reasoning for dynamic knowledge graphs. In ICML. 3462\u20133471."},{"issue":"11","key":"e_1_3_2_24_2","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Maaten Laurens Van der","year":"2008","unstructured":"Laurens Van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. Journal of Machine Learning Research 9, 11 (2008), 2579\u20132605.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_25_2","first-page":"1","volume-title":"ICLR","author":"Vashishth Shikhar","year":"2020","unstructured":"Shikhar Vashishth, Soumya Sanyal, Vikram Nitin, and Partha Talukdar. 2020. Composition-based multi-relational graph convolutional networks. In ICLR, 1\u201316."},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401133"},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.emnlp-main.462"},{"key":"e_1_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3532058"},{"issue":"4","key":"e_1_3_2_29_2","first-page":"1843","article-title":"LBSN2Vec++: Heterogeneous hypergraph embedding for location-based social networks","volume":"34","author":"Yang Dingqi","year":"2020","unstructured":"Dingqi Yang, Bingqing Qu, Jie Yang, and Philippe Cudr\u00e9-Mauroux. 2020. LBSN2Vec++: Heterogeneous hypergraph embedding for location-based social networks. IEEE Transactions on Knowledge and Data Engineering 34, 4 (2020), 1843\u20131855.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539342"},{"key":"e_1_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403389"},{"key":"e_1_3_2_32_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE53745.2022.00167"},{"key":"e_1_3_2_33_2","first-page":"4801","volume-title":"NIPS","author":"Ying Zhitao","year":"2018","unstructured":"Zhitao Ying, Jiaxuan You, Christopher Morris, Xiang Ren, Will Hamilton, and Jure Leskovec. 2018. Hierarchical graph representation learning with differentiable pooling. In NIPS. 4801\u20134811."},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.17323\/1996-7845-2017-01-81"},{"issue":"9","key":"e_1_3_2_35_2","first-page":"1","article-title":"Temporal knowledge graph representation learning with local and global evolutions","volume":"251","author":"Zhang Jiasheng","year":"2022","unstructured":"Jiasheng Zhang, Shuang Liang, Yongpan Sheng, and Jie Shao. 2022. Temporal knowledge graph representation learning with local and global evolutions. Knowledge-Based Systems 251, 9 (2022), 1\u201313.","journal-title":"Knowledge-Based Systems"},{"key":"e_1_3_2_36_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP43922.2022.9747687"},{"issue":"1","key":"e_1_3_2_37_2","first-page":"545","article-title":"Hierarchical multi-view graph pooling with structure learning","volume":"35","author":"Zhang Zhen","year":"2021","unstructured":"Zhen Zhang, Jiajun Bu, Martin Ester, Jianfeng Zhang, Zhao Li, Chengwei Yao, Huifen Dai, Zhi Yu, and Can Wang. 2021. Hierarchical multi-view graph pooling with structure learning. IEEE Transactions on Knowledge and Data Engineering 35, 1 (2021), 545\u2013559.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_2_38_2","first-page":"1614","volume-title":"ICML","author":"Martins Andre","year":"2016","unstructured":"Andre Martins and Ramon Astudillo. 2016. From softmax to sparsemax: A sparse model of attention and multi-label classification. In ICML. 1614\u20131623."},{"key":"e_1_3_2_39_2","doi-asserted-by":"publisher","DOI":"10.1088\/1749-4699\/8\/1\/014008"},{"key":"e_1_3_2_40_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-021-02672-0"},{"issue":"2","key":"e_1_3_2_41_2","first-page":"1","article-title":"Adversarial auto-encoder domain adaptation for cold-start recommendation with positive and negative hypergraphs","volume":"41","author":"Wu Hanrui","year":"2022","unstructured":"Hanrui Wu, Jinyi Long, Nuosi Li, Dahai Yu, and Michael K. Ng. 2022. Adversarial auto-encoder domain adaptation for cold-start recommendation with positive and negative hypergraphs. ACM Transactions on Information Systems 41, 2 (2022), 1\u201325.","journal-title":"ACM Transactions on Information Systems"},{"key":"e_1_3_2_42_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33013060"},{"key":"e_1_3_2_43_2","doi-asserted-by":"publisher","DOI":"10.1145\/3457949"},{"key":"e_1_3_2_44_2","first-page":"5998","volume-title":"NIPS","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, \u0141ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In NIPS. 5998\u20136008."},{"key":"e_1_3_2_45_2","doi-asserted-by":"publisher","DOI":"10.1145\/3363574"},{"issue":"8","key":"e_1_3_2_46_2","first-page":"1658","article-title":"QueryFormer: A tree transformer model for query plan representation","volume":"15","author":"Zhao Yue","year":"2022","unstructured":"Yue Zhao, Gao Cong, Jiachen Shi, and Chunyan Miao. 2022. QueryFormer: A tree transformer model for query plan representation. In VLDB 15, 8 (2022), 1658\u20131670.","journal-title":"VLDB"},{"key":"e_1_3_2_47_2","first-page":"2787","volume-title":"NIPS","author":"Bordes Antoine","year":"2013","unstructured":"Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko. 2013. Translating embeddings for modeling multi-relational data. In NIPS. 2787\u20132795."},{"key":"e_1_3_2_48_2","doi-asserted-by":"publisher","unstructured":"Elizabeth Boschee Jennifer Lautenschlager Sean O'Brien Steve Shellman James Starz and Michael Ward. 2015. ICEWS Coded Event Data. Harvard Dataverse. DOI:10.7910\/DVN\/28075","DOI":"10.7910\/DVN\/28075"},{"key":"e_1_3_2_49_2","doi-asserted-by":"publisher","DOI":"10.1145\/3308560.3316707"},{"key":"e_1_3_2_50_2","doi-asserted-by":"publisher","DOI":"10.1145\/1921632.1921636"}],"container-title":["ACM Transactions on Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3653015","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3653015","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:17:59Z","timestamp":1750295879000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3653015"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,29]]},"references-count":49,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2024,9,30]]}},"alternative-id":["10.1145\/3653015"],"URL":"https:\/\/doi.org\/10.1145\/3653015","relation":{},"ISSN":["1046-8188","1558-2868"],"issn-type":[{"value":"1046-8188","type":"print"},{"value":"1558-2868","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,29]]},"assertion":[{"value":"2023-04-27","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-03-06","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-04-29","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}