{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T16:05:34Z","timestamp":1775837134350,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":35,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,10,21]],"date-time":"2023-10-21T00:00:00Z","timestamp":1697846400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"National Natural Science Foundation of China","award":["No.62072077,No.62176043"],"award-info":[{"award-number":["No.62072077,No.62176043"]}]},{"name":"Natural Science Foun- dation of Sichuan Province","award":["No.2022NSFSC0505"],"award-info":[{"award-number":["No.2022NSFSC0505"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,10,21]]},"DOI":"10.1145\/3583780.3615230","type":"proceedings-article","created":{"date-parts":[[2023,10,21]],"date-time":"2023-10-21T07:45:42Z","timestamp":1697874342000},"page":"3808-3812","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":20,"title":["Enhancing Information Diffusion Prediction with Self-Supervised Disentangled User and Cascade Representations"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0097-3617","authenticated-orcid":false,"given":"Zhangtao","family":"Cheng","sequence":"first","affiliation":[{"name":"University of Electronic Science and Technology of China, Chengdu, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-2853-0697","authenticated-orcid":false,"given":"Wenxue","family":"Ye","sequence":"additional","affiliation":[{"name":"University of Electronic Science and Technology of China, Chengdu, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1179-4769","authenticated-orcid":false,"given":"Leyuan","family":"Liu","sequence":"additional","affiliation":[{"name":"University of Electronic Science and Technology of China, Chengdu, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7364-8324","authenticated-orcid":false,"given":"Wenxin","family":"Tai","sequence":"additional","affiliation":[{"name":"University of Electronic Science and Technology of China & Kash Instiute of Electronics and Information Industry, Chengdu & Kashgar, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8038-8150","authenticated-orcid":false,"given":"Fan","family":"Zhou","sequence":"additional","affiliation":[{"name":"University of Electronic Science and Technology of ChinaUniversity of Electronic Science and Technology of China & Kash Instiute of Electronics and Information Industry, Chengdu & Kashgar, China"}]}],"member":"320","published-online":{"date-parts":[[2023,10,21]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"crossref","unstructured":"Nicola Barbieri Francesco Bonchi and Giuseppe Manco. 2013. Influence-based network-oblivious community detection. In ICDM. 955--960.  Nicola Barbieri Francesco Bonchi and Giuseppe Manco. 2013. Influence-based network-oblivious community detection. In ICDM. 955--960.","DOI":"10.1109\/ICDM.2013.164"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"crossref","unstructured":"Zhangtao Cheng JoojoWalker Ting Zhong and Fan Zhou. 2022. Modeling multiview interactions with contrastive graph learning for collaborative filtering. In IJCNN. 1--8.  Zhangtao Cheng JoojoWalker Ting Zhong and Fan Zhou. 2022. Modeling multiview interactions with contrastive graph learning for collaborative filtering. In IJCNN. 1--8.","DOI":"10.1109\/IJCNN55064.2022.9892152"},{"key":"e_1_3_2_1_3_1","volume-title":"Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555","author":"Chung Junyoung","year":"2014","unstructured":"Junyoung Chung , Caglar Gulcehre , KyungHyun Cho , and Yoshua Bengio . 2014. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 ( 2014 ). Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. 2014. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 (2014)."},{"key":"e_1_3_2_1_4_1","unstructured":"PEOPLE'S DAILY. 2022. Weibo Dataset. http:\/\/data.sklccc.com\/2022\/.  PEOPLE'S DAILY. 2022. Weibo Dataset. http:\/\/data.sklccc.com\/2022\/."},{"key":"e_1_3_2_1_5_1","unstructured":"Yann N Dauphin Angela Fan Michael Auli and David Grangier. 2017. Language modeling with gated convolutional networks. In ICML. 933--941.  Yann N Dauphin Angela Fan Michael Auli and David Grangier. 2017. Language modeling with gated convolutional networks. In ICML. 933--941."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"crossref","unstructured":"Kaize Ding Jianling Wang Jundong Li Dingcheng Li and Huan Liu. 2020. Be More with Less: Hypergraph Attention Networks for Inductive Text Classification. In EMNLP. 4927--4936.  Kaize Ding Jianling Wang Jundong Li Dingcheng Li and Huan Liu. 2020. Be More with Less: Hypergraph Attention Networks for Inductive Text Classification. In EMNLP. 4927--4936.","DOI":"10.18653\/v1\/2020.emnlp-main.399"},{"key":"e_1_3_2_1_7_1","volume-title":"Manuel Gomez Rodriguez, and Hongyuan Zha","author":"Du Nan","year":"2013","unstructured":"Nan Du , Le Song , Manuel Gomez Rodriguez, and Hongyuan Zha . 2013 . Scalable influence estimation in continuous-time diffusion networks. Nips 26 (2013). Nan Du, Le Song, Manuel Gomez Rodriguez, and Hongyuan Zha. 2013. Scalable influence estimation in continuous-time diffusion networks. Nips 26 (2013)."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"crossref","unstructured":"Yifan Feng Haoxuan You Zizhao Zhang Rongrong Ji and Yue Gao. 2019. Hypergraph Neural Networks. In AAAI. 3558--3565.  Yifan Feng Haoxuan You Zizhao Zhang Rongrong Ji and Yue Gao. 2019. Hypergraph Neural Networks. In AAAI. 3558--3565.","DOI":"10.1609\/aaai.v33i01.33013558"},{"key":"e_1_3_2_1_9_1","unstructured":"Manuel Gomez-Rodriguez David Balduzzi and Bernhard Sch\u00f6lkopf. 2011. Uncovering the Temporal Dynamics of Diffusion Networks. In ICML Lise Getoor and Tobias Scheffer (Eds.). 561--568.  Manuel Gomez-Rodriguez David Balduzzi and Bernhard Sch\u00f6lkopf. 2011. Uncovering the Temporal Dynamics of Diffusion Networks. In ICML Lise Getoor and Tobias Scheffer (Eds.). 561--568."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401063"},{"key":"e_1_3_2_1_11_1","volume-title":"The simple rules of social contagion. Scientific reports 4, 1","author":"Hodas Nathan O","year":"2014","unstructured":"Nathan O Hodas and Kristina Lerman . 2014. The simple rules of social contagion. Scientific reports 4, 1 ( 2014 ), 1--7. Nathan O Hodas and Kristina Lerman. 2014. The simple rules of social contagion. Scientific reports 4, 1 (2014), 1--7."},{"key":"e_1_3_2_1_12_1","volume-title":"Deepdiffuse: Predicting the'who'and'when'in cascades. In ICDM. 1055--1060.","author":"Islam Mohammad Raihanul","year":"2018","unstructured":"Mohammad Raihanul Islam , Sathappan Muthiah , Bijaya Adhikari , B Aditya Prakash , and Naren Ramakrishnan . 2018 . Deepdiffuse: Predicting the'who'and'when'in cascades. In ICDM. 1055--1060. Mohammad Raihanul Islam, Sathappan Muthiah, Bijaya Adhikari, B Aditya Prakash, and Naren Ramakrishnan. 2018. Deepdiffuse: Predicting the'who'and'when'in cascades. In ICDM. 1055--1060."},{"key":"e_1_3_2_1_13_1","volume-title":"Rankmi: A mutual information maximizing ranking loss. In CVPR. 14362--14371.","author":"Kemertas Mete","year":"2020","unstructured":"Mete Kemertas , Leila Pishdad , Konstantinos G Derpanis , and Afsaneh Fazly . 2020 . Rankmi: A mutual information maximizing ranking loss. In CVPR. 14362--14371. Mete Kemertas, Leila Pishdad, Konstantinos G Derpanis, and Afsaneh Fazly. 2020. Rankmi: A mutual information maximizing ranking loss. In CVPR. 14362--14371."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"crossref","unstructured":"David Kempe Jon Kleinberg and \u00c9va Tardos. 2003. Maximizing the spread of influence through a social network. In SIGKDD. 137--146.  David Kempe Jon Kleinberg and \u00c9va Tardos. 2003. Maximizing the spread of influence through a social network. In SIGKDD. 137--146.","DOI":"10.1145\/956750.956769"},{"key":"e_1_3_2_1_15_1","volume-title":"Kipf and Max Welling","author":"Thomas","year":"2017","unstructured":"Thomas N. Kipf and Max Welling . 2017 . Semi-Supervised Classification with Graph Convolutional Networks. In ICLR. Thomas N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In ICLR."},{"key":"e_1_3_2_1_16_1","first-page":"21872","article-title":"Disentangled contrastive learning on graphs","volume":"34","author":"Li Haoyang","year":"2021","unstructured":"Haoyang Li , Xin Wang , Ziwei Zhang , Zehuan Yuan , Hang Li , and Wenwu Zhu . 2021 . Disentangled contrastive learning on graphs . Neurips 34 (2021), 21872 -- 21884 . Haoyang Li, Xin Wang, Ziwei Zhang, Zehuan Yuan, Hang Li, and Wenwu Zhu. 2021. Disentangled contrastive learning on graphs. Neurips 34 (2021), 21872-- 21884.","journal-title":"Neurips"},{"key":"e_1_3_2_1_17_1","volume-title":"Birds of a feather: Homophily in social networks. Annual review of sociology 27, 1","author":"McPherson Miller","year":"2001","unstructured":"Miller McPherson , Lynn Smith-Lovin , and JamesMCook. 2001. Birds of a feather: Homophily in social networks. Annual review of sociology 27, 1 ( 2001 ), 415--444. Miller McPherson, Lynn Smith-Lovin, and JamesMCook. 2001. Birds of a feather: Homophily in social networks. Annual review of sociology 27, 1 (2001), 415--444."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1017\/nws.2014.3"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/3336191.3371811"},{"key":"e_1_3_2_1_20_1","volume-title":"Homophily and contagion are generically confounded in observational social network studies. Sociological methods & research 40, 2","author":"Shalizi Cosma Rohilla","year":"2011","unstructured":"Cosma Rohilla Shalizi and Andrew C Thomas . 2011. Homophily and contagion are generically confounded in observational social network studies. Sociological methods & research 40, 2 ( 2011 ), 211--239. Cosma Rohilla Shalizi and Andrew C Thomas. 2011. Homophily and contagion are generically confounded in observational social network studies. Sociological methods & research 40, 2 (2011), 211--239."},{"key":"e_1_3_2_1_21_1","volume-title":"MSHGAT: Memory-enhanced Sequential Hypergraph Attention Network for Information Diffusion Prediction. In AAAI.","author":"Sun Ling","year":"2022","unstructured":"Ling Sun , Yuan Rao , Xiangbo Zhang , Yuqian Lan , and Shuanghe Yu . 2022 . MSHGAT: Memory-enhanced Sequential Hypergraph Attention Network for Information Diffusion Prediction. In AAAI. Ling Sun, Yuan Rao, Xiangbo Zhang, Yuqian Lan, and Shuanghe Yu. 2022. MSHGAT: Memory-enhanced Sequential Hypergraph Attention Network for Information Diffusion Prediction. In AAAI."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11257-022-09332-z"},{"key":"e_1_3_2_1_23_1","unstructured":"Ashish Vaswani Noam Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan N. Gomez Lukasz Kaiser and Illia Polosukhin. 2017. Attention is All you Need. In NIPS. 5998--6008.  Ashish Vaswani Noam Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan N. Gomez Lukasz Kaiser and Illia Polosukhin. 2017. Attention is All you Need. In NIPS. 5998--6008."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"crossref","unstructured":"Jia Wang Vincent W Zheng Zemin Liu and Kevin Chen-Chuan Chang. 2017. Topological recurrent neural network for diffusion prediction. In ICDM. 475--484.  Jia Wang Vincent W Zheng Zemin Liu and Kevin Chen-Chuan Chang. 2017. Topological recurrent neural network for diffusion prediction. In ICDM. 475--484.","DOI":"10.1109\/ICDM.2017.57"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"crossref","unstructured":"Zhitao Wang Chengyao Chen and Wenjie Li. 2018. A sequential neural information diffusion model with structure attention. In CIKM. 1795--1798.  Zhitao Wang Chengyao Chen and Wenjie Li. 2018. A sequential neural information diffusion model with structure attention. In CIKM. 1795--1798.","DOI":"10.1145\/3269206.3269275"},{"key":"e_1_3_2_1_26_1","unstructured":"Felix Wu Amauri Souza Tianyi Zhang Christopher Fifty Tao Yu and Kilian Weinberger. 2019. Simplifying graph convolutional networks. In ICML. 6861-- 6871.  Felix Wu Amauri Souza Tianyi Zhang Christopher Fifty Tao Yu and Kilian Weinberger. 2019. Simplifying graph convolutional networks. In ICML. 6861-- 6871."},{"key":"e_1_3_2_1_27_1","volume-title":"Casflow: Exploring hierarchical structures and propagation uncertainty for cascade prediction","author":"Xu Xovee","year":"2021","unstructured":"Xovee Xu , Fan Zhou , Kunpeng Zhang , Siyuan Liu , and Goce Trajcevski . 2021 . Casflow: Exploring hierarchical structures and propagation uncertainty for cascade prediction . IEEE TKDE ( 2021). Xovee Xu, Fan Zhou, Kunpeng Zhang, Siyuan Liu, and Goce Trajcevski. 2021. Casflow: Exploring hierarchical structures and propagation uncertainty for cascade prediction. IEEE TKDE (2021)."},{"key":"e_1_3_2_1_28_1","first-page":"1128","article-title":"Neural diffusion model for microscopic cascade study","volume":"33","author":"Yang Cheng","year":"2019","unstructured":"Cheng Yang , Maosong Sun , Haoran Liu , Shiyi Han , Zhiyuan Liu , and Huanbo Luan . 2019 . Neural diffusion model for microscopic cascade study . IEEE TKDE 33 , 3 (2019), 1128 -- 1139 . Cheng Yang, Maosong Sun, Haoran Liu, Shiyi Han, Zhiyuan Liu, and Huanbo Luan. 2019. Neural diffusion model for microscopic cascade study. IEEE TKDE 33, 3 (2019), 1128--1139.","journal-title":"IEEE TKDE"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"crossref","unstructured":"Cheng Yang Jian Tang Maosong Sun Ganqu Cui and Zhiyuan Liu. 2019. Multiscale Information Diffusion Prediction with Reinforced Recurrent Networks. In IJCAI. 4033--4039.  Cheng Yang Jian Tang Maosong Sun Ganqu Cui and Zhiyuan Liu. 2019. Multiscale Information Diffusion Prediction with Reinforced Recurrent Networks. In IJCAI. 4033--4039.","DOI":"10.24963\/ijcai.2019\/560"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.109740"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"crossref","unstructured":"Chunyuan Yuan Jiacheng Li Wei Zhou Yijun Lu Xiaodan Zhang and Songlin Hu. 2020. DyHGCN: A dynamic heterogeneous graph convolutional network to learn users' dynamic preferences for information diffusion prediction. In ECML-PKDD. 347--363.  Chunyuan Yuan Jiacheng Li Wei Zhou Yijun Lu Xiaodan Zhang and Songlin Hu. 2020. DyHGCN: A dynamic heterogeneous graph convolutional network to learn users' dynamic preferences for information diffusion prediction. In ECML-PKDD. 347--363.","DOI":"10.1007\/978-3-030-67664-3_21"},{"key":"e_1_3_2_1_32_1","volume-title":"Structinf: Mining structural influence from social streams. In AAAI.","author":"Zhang Jing","year":"2017","unstructured":"Jing Zhang , Jie Tang , Yuanyi Zhong , Yuchen Mo , Juanzi Li , Guojie Song , Wendy Hall , and Jimeng Sun . 2017 . Structinf: Mining structural influence from social streams. In AAAI. Jing Zhang, Jie Tang, Yuanyi Zhong, Yuchen Mo, Juanzi Li, Guojie Song, Wendy Hall, and Jimeng Sun. 2017. Structinf: Mining structural influence from social streams. In AAAI."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/3316481"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/3433000"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"crossref","unstructured":"Fan Zhou Liu Yu Xovee Xu and Goce Trajcevski. 2021. Decoupling representation and regressor for long-tailed information cascade prediction. In SIGIR.  Fan Zhou Liu Yu Xovee Xu and Goce Trajcevski. 2021. Decoupling representation and regressor for long-tailed information cascade prediction. In SIGIR.","DOI":"10.1145\/3404835.3463104"}],"event":{"name":"CIKM '23: The 32nd ACM International Conference on Information and Knowledge Management","location":"Birmingham United Kingdom","acronym":"CIKM '23","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 32nd ACM International Conference on Information and Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3583780.3615230","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3583780.3615230","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:36:58Z","timestamp":1750178218000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3583780.3615230"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,21]]},"references-count":35,"alternative-id":["10.1145\/3583780.3615230","10.1145\/3583780"],"URL":"https:\/\/doi.org\/10.1145\/3583780.3615230","relation":{},"subject":[],"published":{"date-parts":[[2023,10,21]]},"assertion":[{"value":"2023-10-21","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}