{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T21:48:56Z","timestamp":1780523336275,"version":"3.54.1"},"publisher-location":"Singapore","reference-count":25,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819605729","type":"print"},{"value":"9789819605736","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T00:00:00Z","timestamp":1732665600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T00:00:00Z","timestamp":1732665600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-981-96-0573-6_6","type":"book-chapter","created":{"date-parts":[[2024,11,26]],"date-time":"2024-11-26T06:09:21Z","timestamp":1732601361000},"page":"76-89","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Equivariant Diffusion-Based Sequential Hypergraph Neural Networks with\u00a0Co-attention Fusion for\u00a0Information Diffusion Prediction"],"prefix":"10.1007","author":[{"given":"Ye","family":"Lu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ji","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ting","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gaoming","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,11,27]]},"reference":[{"key":"6_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107637","volume":"110","author":"S Bai","year":"2021","unstructured":"Bai, S., Zhang, F., Torr, P.H.: Hypergraph convolution and hypergraph attention. Pattern Recogn. 110, 107637 (2021)","journal-title":"Pattern Recogn."},{"key":"6_CR2","doi-asserted-by":"crossref","unstructured":"Cheng, J., Adamic, L., Dow, P.A., Kleinberg, J.M., Leskovec, J.: Can cascades be predicted? In: WWW, pp. 925\u2013936 (2014)","DOI":"10.1145\/2566486.2567997"},{"key":"6_CR3","doi-asserted-by":"crossref","unstructured":"Feng, Y., You, H., Zhang, Z., Ji, R., Gao, Y.: Hypergraph neural networks. In: AAAI, pp. 3558\u20133565 (2019)","DOI":"10.1609\/aaai.v33i01.33013558"},{"issue":"2","key":"6_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3301303","volume":"13","author":"X Gao","year":"2019","unstructured":"Gao, X., Cao, Z., Li, S., Yao, B., Chen, G., Tang, S.: Taxonomy and evaluation for microblog popularity prediction. ACM TKDD 13(2), 1\u201340 (2019)","journal-title":"ACM TKDD"},{"issue":"1","key":"6_CR5","doi-asserted-by":"publisher","first-page":"4343","DOI":"10.1038\/srep04343","volume":"4","author":"NO Hodas","year":"2014","unstructured":"Hodas, N.O., Lerman, K.: The simple rules of social contagion. Sci. Rep. 4(1), 4343 (2014)","journal-title":"Sci. Rep."},{"key":"6_CR6","doi-asserted-by":"crossref","unstructured":"Islam, M.R., Muthiah, S., Adhikari, B., Prakash, B.A., Ramakrishnan, N.: DeepDiffuse: predicting the \u2018who\u2019 and \u2018when\u2019 in cascades. In: ICDM, pp. 1055\u20131060. IEEE (2018)","DOI":"10.1109\/ICDM.2018.00134"},{"key":"6_CR7","doi-asserted-by":"crossref","unstructured":"Kempe, D., Kleinberg, J., Tardos, \u00c9.: Maximizing the spread of influence through a social network, pp. 137\u2013146. ACM (2003)","DOI":"10.1145\/956750.956769"},{"key":"6_CR8","unstructured":"Lu, J., Batra, D., Parikh, D., Lee, S.: ViLBERT: pretraining task-agnostic visiolinguistic representations for vision-and-language tasks. In: Advances in Neural Information Processing Systems, vol. 32 (2019)"},{"key":"6_CR9","doi-asserted-by":"crossref","unstructured":"Sankar, A., Zhang, X., Krishnan, A., Han, J.: Inf-VAE: a variational autoencoder framework to integrate homophily and influence in diffusion prediction. In: WSDM, pp. 510\u2013518 (2020)","DOI":"10.1145\/3336191.3371811"},{"key":"6_CR10","doi-asserted-by":"crossref","unstructured":"Sun, L., Rao, Y., Zhang, X., Lan, Y., Yu, S.: MS-HGAT: memory-enhanced sequential hypergraph attention network for information diffusion prediction. In: AAAI, pp. 4156\u20134164 (2022)","DOI":"10.1609\/aaai.v36i4.20334"},{"key":"6_CR11","doi-asserted-by":"crossref","unstructured":"Tsur, O., Rappoport, A.: What\u2019s in a hashtag? Content based prediction of the spread of ideas in microblogging communities. In: WSDM, pp. 643\u2013652 (2012)","DOI":"10.1145\/2124295.2124320"},{"key":"6_CR12","doi-asserted-by":"crossref","unstructured":"Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146\u20131151 (2018)","DOI":"10.1126\/science.aap9559"},{"key":"6_CR13","doi-asserted-by":"crossref","unstructured":"Wang, J., Zheng, V.W., Liu, Z., Chang, K.C.C.: Topological recurrent neural network for diffusion prediction. In: ICDM, pp. 475\u2013484. IEEE (2017)","DOI":"10.1109\/ICDM.2017.57"},{"key":"6_CR14","doi-asserted-by":"crossref","unstructured":"Wang, J., Ding, K., Hong, L., Liu, H., Caverlee, J.: Next-item recommendation with sequential hypergraphs. In: SIGIR, pp. 1101\u20131110 (2020)","DOI":"10.1145\/3397271.3401133"},{"issue":"10","key":"6_CR15","first-page":"18557","volume":"23","author":"J Wang","year":"2022","unstructured":"Wang, J., Zhang, Y., Wang, L., Hu, Y., Piao, X., Yin, B.: Multitask hypergraph convolutional networks: a heterogeneous traffic prediction framework. IEEE TITS 23(10), 18557\u201318567 (2022)","journal-title":"IEEE TITS"},{"key":"6_CR16","unstructured":"Wang, P., Yang, S., Liu, Y., Wang, Z., Li, P.: Equivariant hypergraph diffusion neural operators. arXiv preprint arXiv:2207.06680 (2022)"},{"key":"6_CR17","doi-asserted-by":"crossref","unstructured":"Wang, Y., Shen, H., Liu, S., Gao, J., Cheng, X.: Cascade dynamics modeling with attention-based recurrent neural network. In: IJCAI. vol.\u00a017, pp. 2985\u20132991 (2017)","DOI":"10.24963\/ijcai.2017\/416"},{"key":"6_CR18","doi-asserted-by":"crossref","unstructured":"Wang, Z., Chen, C., Li, W.: A sequential neural information diffusion model with structure attention. In: CIKM, pp. 1795\u20131798 (2018)","DOI":"10.1145\/3269206.3269275"},{"key":"6_CR19","doi-asserted-by":"crossref","unstructured":"Wu, Q., Gao, Y., Gao, X., Weng, P., Chen, G.: Dual sequential prediction models linking sequential recommendation and information dissemination. In: ACM SIGKDD, pp. 447\u2013457 (2019)","DOI":"10.1145\/3292500.3330959"},{"key":"6_CR20","unstructured":"Yadati, N., Nimishakavi, M., Yadav, P., Nitin, V., Louis, A., Talukdar, P.: HyperGCN: a new method for training graph convolutional networks on hypergraphs. In: Advances in Neural Information Processing Systems, vol. 32 (2019)"},{"key":"6_CR21","doi-asserted-by":"crossref","unstructured":"Yang, C., Sun, M., Liu, H., Han, S., Liu, Z., Luan, H.: Neural Diffusion Model for Microscopic Cascade Prediction (2018)","DOI":"10.1109\/TKDE.2019.2939796"},{"issue":"5","key":"6_CR22","first-page":"2271","volume":"34","author":"C Yang","year":"2021","unstructured":"Yang, C., et al.: Full-scale information diffusion prediction with reinforced recurrent networks. IEEE TNNLS 34(5), 2271\u20132283 (2021)","journal-title":"IEEE TNNLS"},{"key":"6_CR23","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"347","DOI":"10.1007\/978-3-030-67664-3_21","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"C Yuan","year":"2021","unstructured":"Yuan, C., Li, J., Zhou, W., Lu, Y., Zhang, X., Hu, S.: DyHGCN: a dynamic heterogeneous graph convolutional network to learn users\u2019 dynamic preferences for information diffusion prediction. In: Hutter, F., Kersting, K., Lijffijt, J., Valera, I. (eds.) ECML PKDD 2020. LNCS (LNAI), vol. 12459, pp. 347\u2013363. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-67664-3_21"},{"key":"6_CR24","unstructured":"Zhang, R., Zou, Y., Ma, J.: Hyper-SAGNN: a self-attention based graph neural network for hypergraphs. arXiv preprint arXiv:1911.02613 (2019)"},{"key":"6_CR25","doi-asserted-by":"crossref","unstructured":"Zhong, E., Fan, W., Wang, J., Xiao, L., Li, Y.: ComSoc: adaptive transfer of user behaviors over composite social network. In: ACM SIGKDD, pp. 696\u2013704 (2012)","DOI":"10.1145\/2339530.2339641"}],"container-title":["Lecture Notes in Computer Science","Web Information Systems Engineering \u2013 WISE 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-0573-6_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,26]],"date-time":"2024-11-26T08:44:32Z","timestamp":1732610672000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-0573-6_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,27]]},"ISBN":["9789819605729","9789819605736"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-0573-6_6","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,27]]},"assertion":[{"value":"27 November 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"WISE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Web Information Systems Engineering","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Doha","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Qatar","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 December 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 December 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"wise2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/wise2024-qatar.com\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}