{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T18:00:11Z","timestamp":1772906411120,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":24,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,7,11]],"date-time":"2021-07-11T00:00:00Z","timestamp":1625961600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000001","name":"NSF (National Science Foundation)","doi-asserted-by":"publisher","award":["1646107"],"award-info":[{"award-number":["1646107"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61602097"],"award-info":[{"award-number":["61602097"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,7,11]]},"DOI":"10.1145\/3404835.3463104","type":"proceedings-article","created":{"date-parts":[[2021,7,12]],"date-time":"2021-07-12T02:41:54Z","timestamp":1626057714000},"page":"1875-1879","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":18,"title":["Decoupling Representation and Regressor for Long-Tailed Information Cascade Prediction"],"prefix":"10.1145","author":[{"given":"Fan","family":"Zhou","sequence":"first","affiliation":[{"name":"University of Electronic Science and Technology of China, Chengdu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liu","family":"Yu","sequence":"additional","affiliation":[{"name":"University of Electronic Science and Technology of China, Chengdu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xovee","family":"Xu","sequence":"additional","affiliation":[{"name":"University of Electronic Science and Technology of China, Chengdu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Goce","family":"Trajcevski","sequence":"additional","affiliation":[{"name":"Iowa State University, Ames, IA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,7,11]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"Kaidi Cao Colin Wei Adrien Gaidon Nikos Arechiga and Tengyu Ma. 2019. Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss. In NeurIPS. 18 pages."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"crossref","unstructured":"Qi Cao Huawei Shen Keting Cen Wentao Ouyang and Xueqi Cheng. 2017. DeepHawkes: Bridging the gap between prediction and understanding of information cascades. In CIKM. 1149--1158.","DOI":"10.1145\/3132847.3132973"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"crossref","unstructured":"Qi Cao Huawei Shen Jinhua Gao Bingzheng Wei and Xueqi Cheng. 2020. Popularity prediction on social platforms with coupled graph neural networks. In WSDM. 70--78.","DOI":"10.1145\/3336191.3371834"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1613\/jair.953"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"crossref","unstructured":"Xueqin Chen Fan Zhou Kunpeng Zhang Goce Trajcevski Ting Zhong and Fengli Zhang. 2019. Information diffusion prediction via recurrent cascades convolution. In ICDE. 770--781.","DOI":"10.1109\/ICDE.2019.00074"},{"key":"e_1_3_2_1_6_1","volume-title":"Jon Michael Kleinberg, and Jure Leskovec","author":"Cheng Justin","year":"2014","unstructured":"Justin Cheng, Lada Adamic, P Alex Dow, Jon Michael Kleinberg, and Jure Leskovec. 2014. Can cascades be predicted?. In WWW. 925--936."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"crossref","unstructured":"Yin Cui Yang Song Chen Sun Andrew Howard and Serge Belongie. 2018. Large scale fine-grained categorization and domain-specific transfer learning. In CVPR. 4109--4118.","DOI":"10.1109\/CVPR.2018.00432"},{"key":"e_1_3_2_1_8_1","unstructured":"Bingyi Kang Saining Xie Marcus Rohrbach Zhicheng Yan Albert Gordo Jiashi Feng and Yannis Kalantidis. 2020. Decoupling representation and classifier for long-tailed recognition. In ICLR. 16 pages."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"crossref","unstructured":"Quyu Kong Marian-Andrei Rizoiu and Lexing Xie. 2020. Describing and Predicting Online Items with Reshare Cascades via Dual Mixture Self-exciting Processes. In CIKM. 645--654.","DOI":"10.1145\/3340531.3411861"},{"key":"e_1_3_2_1_10_1","unstructured":"Cheng Li Jiaqi Ma Xiaoxiao Guo and Qiaozhu Mei. 2017. DeepCas: An end-to-end predictor of information cascades. In WWW. 577--586."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113785"},{"key":"e_1_3_2_1_12_1","volume-title":"Hate is the New Infodemic: A Topic-aware Modeling of Hate Speech Diffusion on Twitter. arXiv:2010.04377","author":"Masud Sarah","year":"2020","unstructured":"Sarah Masud, Subhabrata Dutta, Sakshi Makkar, Chhavi Jain, Vikram Goyal, Amitava Das, and Tanmoy Chakraborty. 2020. Hate is the New Infodemic: A Topic-aware Modeling of Hate Speech Diffusion on Twitter. arXiv:2010.04377 (2020), 12 pages."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"crossref","unstructured":"Swapnil Mishra Marian-Andrei Rizoiu and Lexing Xie. 2016. Feature driven and point process approaches for popularity prediction. In CIKM. 1069--1078.","DOI":"10.1145\/2983323.2983812"},{"key":"e_1_3_2_1_14_1","unstructured":"Amandianeze O Nwana Salman Avestimehr and Tsuhan Chen. 2013. A latent social approach to youtube popularity prediction. In GLOBECOM. 3138--3144."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"crossref","unstructured":"Marian-Andrei Rizoiu Swapnil Mishra Quyu Kong Mark Carman and Lexing Xie. 2018. SIR-Hawkes: Linking epidemic models and Hawkes processes to model diffusions in finite populations. In WWW. 419--428.","DOI":"10.1145\/3178876.3186108"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"crossref","unstructured":"Oren Tsur and Ari Rappoport. 2012. What's in a hashtag? Content based prediction of the spread of ideas in microblogging communities. In WSDM. 643--652.","DOI":"10.1145\/2124295.2124320"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"crossref","unstructured":"Yongqing Wang Huawei Shen Shenghua Liu Jinhua Gao and Xueqi Cheng. 2017b. Cascade Dynamics Modeling with Attention-based Recurrent Neural Network.. In IJCAI. 2985--2991.","DOI":"10.24963\/ijcai.2017\/416"},{"key":"e_1_3_2_1_18_1","unstructured":"Yu-Xiong Wang Deva Ramanan and Martial Hebert. 2017a. Learning to model the tail. In NIPS. 7032--7042."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1038\/srep02522"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783401"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"crossref","unstructured":"Boyan Zhou Quan Cui Xiu-Shen Wei and Zhao-Min Chen. 2020 a. BBN: Bilateral-branch network with cumulative learning for long-tailed visual recognition. In CVPR. 9719--9728.","DOI":"10.1109\/CVPR42600.2020.00974"},{"key":"e_1_3_2_1_22_1","volume-title":"2020 b. A Heterogeneous Dynamical Graph Neural Networks Approach to Quantify Scientific Impact. arXiv:2003.12042","author":"Zhou Fan","year":"2020","unstructured":"Fan Zhou, Xovee Xu, Ce Li, Goce Trajcevski, Ting Zhong, and Kunpeng Zhang. 2020 b. A Heterogeneous Dynamical Graph Neural Networks Approach to Quantify Scientific Impact. arXiv:2003.12042 (2020), 8 pages."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/3433000"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"crossref","unstructured":"Fan Zhou Xovee Xu Kunpeng Zhang Goce Trajcevski and Ting Zhong. 2020 c. Variational information diffusion for probabilistic cascades prediction. In INFOCOM. 1618--1627.","DOI":"10.1109\/INFOCOM41043.2020.9155349"}],"event":{"name":"SIGIR '21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval","location":"Virtual Event Canada","acronym":"SIGIR '21","sponsor":["SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3404835.3463104","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3404835.3463104","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3404835.3463104","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:18:30Z","timestamp":1750191510000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3404835.3463104"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,11]]},"references-count":24,"alternative-id":["10.1145\/3404835.3463104","10.1145\/3404835"],"URL":"https:\/\/doi.org\/10.1145\/3404835.3463104","relation":{},"subject":[],"published":{"date-parts":[[2021,7,11]]},"assertion":[{"value":"2021-07-11","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}