{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T06:46:29Z","timestamp":1768545989830,"version":"3.49.0"},"publisher-location":"New York, NY, USA","reference-count":46,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,8,24]],"date-time":"2024-08-24T00:00:00Z","timestamp":1724457600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/https:\/\/doi.org\/10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61972272"],"award-info":[{"award-number":["61972272"]}],"id":[{"id":"10.13039\/https:\/\/doi.org\/10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Foundation of the Jiangsu Higher Education Institutions of China","award":["21KJA520008"],"award-info":[{"award-number":["21KJA520008"]}]},{"name":"IBM faculty award"},{"name":"Qinlan Project of Jiangsu Province of China"},{"name":"the National Key R&D Program for the 14th-Five-Year Plan of China","award":["2023YFC3804104 in 2023YFC3804100"],"award-info":[{"award-number":["2023YFC3804104 in 2023YFC3804100"]}]},{"name":"National Science Foundation CISE","award":["2302720, 2312758, 2038029"],"award-info":[{"award-number":["2302720, 2312758, 2038029"]}]},{"name":"the Fundamental Research Funds for the Central Universities"},{"name":"the Priority Academic Program Development of Jiangsu Higher Education Institutions"},{"name":"CISCO Edge AI program"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,8,25]]},"DOI":"10.1145\/3637528.3671966","type":"proceedings-article","created":{"date-parts":[[2024,8,25]],"date-time":"2024-08-25T04:55:12Z","timestamp":1724561712000},"page":"3460-3471","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["A Deep Prediction Framework for Multi-Source Information via Heterogeneous GNN"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-6221-280X","authenticated-orcid":false,"given":"Zhen","family":"Wu","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Soochow University & School of Computer Science and Engineering, Southeast University, Suzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0721-7424","authenticated-orcid":false,"given":"Jingya","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Engineering Lab. of Big Data and Intelligence of Jiangsu Province, Soochow University & State Key Lab. for Novel Software Technology, Nanjing University, Suzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9067-7896","authenticated-orcid":false,"given":"Jinghui","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Southeast University, Nanjing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4138-3082","authenticated-orcid":false,"given":"Ling","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science, Georgia Institute of Technology, Atlanta, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-0248-3253","authenticated-orcid":false,"given":"Chizhou","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Soochow University, Suzhou, China"}]}],"member":"320","published-online":{"date-parts":[[2024,8,24]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/1935826.1935845"},{"key":"e_1_3_2_2_2_1","volume-title":"WWW","author":"Bao P.","year":"2013","unstructured":"P. Bao, H.-W. Shen, J. Huang, and X.-Q. Cheng. Popularity prediction in microblogging network: a case study on sina weibo. In WWW, 2013."},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5393"},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1088\/1742-5468\/2008\/10\/P10008"},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3132847.3132973"},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3336191.3371834"},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2019.00074"},{"key":"e_1_3_2_2_8_1","volume-title":"WWW","author":"Cheng J.","year":"2014","unstructured":"J. Cheng, L. Adamic, P. A. Dow, J. M. Kleinberg, and J. Leskovec. Can cascades be predicted? In WWW, 2014."},{"key":"e_1_3_2_2_9_1","volume-title":"Learning phrase representations using rnn encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078","author":"Cho K.","year":"2014","unstructured":"K. Cho, B. Van Merri\u00ebnboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio. Learning phrase representations using rnn encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078, 2014."},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2012.140"},{"key":"e_1_3_2_2_11_1","volume-title":"WWW","author":"Gao H.","year":"2018","unstructured":"H. Gao, D. Kong, M. Lu, X. Bai, and J. Yang. Attention convolutional neural network for advertiser-level click-through rate forecasting. In WWW, 2018."},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939754"},{"key":"e_1_3_2_2_13_1","volume-title":"WWW","author":"Guille A.","year":"2012","unstructured":"A. Guille and H. Hacid. A predictive model for the temporal dynamics of information diffusion in online social networks. In WWW, 2012."},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_3_2_2_15_1","volume-title":"CIKM","author":"Hopcroft J.","year":"2011","unstructured":"J. Hopcroft, T. Lou, and J. Tang. Who will follow you back? reciprocal relationship prediction. In CIKM, 2011."},{"key":"e_1_3_2_2_16_1","volume-title":"WWW","author":"Jenders M.","year":"2013","unstructured":"M. Jenders, G. Kasneci, and F. Naumann. Analyzing and predicting viral tweets. In WWW, 2013."},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/956750.956769"},{"key":"e_1_3_2_2_18_1","volume-title":"WWW","author":"Kwak H.","year":"2010","unstructured":"H. Kwak, C. Lee, H. Park, and S. Moon. What is twitter, a social network or a news media? In WWW, 2010."},{"key":"e_1_3_2_2_19_1","volume-title":"WWW","author":"Li C.","year":"2017","unstructured":"C. Li, J. Ma, X. Guo, and Q. Mei. DeepCas: An End-to-end Predictor of Information Cascades. In WWW, 2017."},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2018.2807843"},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/2499907.2499908"},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2016.7524377"},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623732"},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220077"},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.5555\/2893873.2893920"},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3336191.3371791"},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2023.103278"},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICNP.2016.7784445"},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2018.8485975"},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i1.16137"},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNET.2016.2563397"},{"key":"e_1_3_2_2_32_1","volume-title":"NeuIPS","author":"Vaswani A.","year":"2017","unstructured":"A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, ?. Kaiser, and I. Polosukhin. Attention is all you need. In NeuIPS, 2017."},{"key":"e_1_3_2_2_33_1","volume-title":"Science","author":"Wang D.","year":"2013","unstructured":"D. Wang, C. Song, and A.-L. Barab\u00e1si. Quantifying long-term scientific impact. Science, 2013."},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2017.57"},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1609\/icwsm.v8i1.14530"},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE53745.2022.00174"},{"key":"e_1_3_2_2_37_1","volume-title":"TKDE","author":"Xu X.","year":"2021","unstructured":"X. Xu, F. Zhou, K. Zhang, S. Liu, and G. Trajcevski. Casflow: Exploring hierarchical structures and propagation uncertainty for cascade prediction. TKDE, 2021."},{"key":"e_1_3_2_2_38_1","volume-title":"Dyhgcn: A dynamic heterogeneous graph convolutional network to learn users' dynamic preferences for information diffusion prediction. arXiv preprint arXiv:2006.05169","author":"Yuan C.","year":"2020","unstructured":"C. Yuan, J. Li, W. Zhou, Y. Lu, X. Zhang, and S. Hu. Dyhgcn: A dynamic heterogeneous graph convolutional network to learn users' dynamic preferences for information diffusion prediction. arXiv preprint arXiv:2006.05169, 2020."},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/2835776.2835800"},{"key":"e_1_3_2_2_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2015.2407351"},{"key":"e_1_3_2_2_41_1","volume-title":"ICML","author":"Zhang X.","year":"2022","unstructured":"X. Zhang, A. Aravamudan, and G. C. Anagnostopoulos. Anytime information cascade popularity prediction via self-exciting processes. In ICML, 2022."},{"key":"e_1_3_2_2_42_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10707-019-00376-9"},{"key":"e_1_3_2_2_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783401"},{"key":"e_1_3_2_2_44_1","volume-title":"CBD","author":"Zhen W.","year":"2022","unstructured":"W. Zhen, Z. Jingya, W. Jie, and S. Xigang. Wb-msf: A large-scale multi-source information diffusion dataset for social information diffusion prediction. In CBD, 2022."},{"key":"e_1_3_2_2_45_1","volume-title":"A Survey of Information Cascade Analysis: Models, Predictions and Recent Advances. arXiv:2005.11041 [cs]","author":"Zhou F.","year":"2020","unstructured":"F. Zhou, X. Xu, G. Trajcevski, and K. Zhang. A Survey of Information Cascade Analysis: Models, Predictions and Recent Advances. arXiv:2005.11041 [cs], 2020."},{"key":"e_1_3_2_2_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3463104"}],"event":{"name":"KDD '24: The 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Barcelona Spain","acronym":"KDD '24","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3637528.3671966","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3637528.3671966","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:06:05Z","timestamp":1750291565000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3637528.3671966"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,24]]},"references-count":46,"alternative-id":["10.1145\/3637528.3671966","10.1145\/3637528"],"URL":"https:\/\/doi.org\/10.1145\/3637528.3671966","relation":{},"subject":[],"published":{"date-parts":[[2024,8,24]]},"assertion":[{"value":"2024-08-24","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}