A cascade information diffusion prediction model integrating topic features and cross-attention

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dc.authoridBouyer, Asgarali/0000-0002-4808-2856
dc.contributor.authorLiu, Xiaoyang
dc.contributor.authorWang, Haotian
dc.contributor.authorBouyer, Asgarali
dc.date.accessioned2024-05-19T14:42:41Z
dc.date.available2024-05-19T14:42:41Z
dc.date.issued2023
dc.departmentİstinye Üniversitesien_US
dc.description.abstractInformation cascade prediction is a crucial task in social network analysis. However, previous research has only focused on the impact of social relationships on cascade information diffusion, while ignoring the differences caused by the characteristics of cascade information itself, which limits the performance of prediction results. We propose a novel cascade information diffusion prediction model (Topic-HGAT). Firstly, we extract features from different topic features to enhance the learned cascade information representation. To better implement this method, we use hypergraphs to better characterize cascade information and dynamically learn multiple diffusion sub-hypergraphs according to the time process; secondly, we introduce cross-attention mechanisms to learn each other's feature representations from the perspectives of both user representation and cascade representation, thereby achieving deep fusion of the two features. This solves the problem of poor feature fusion effect caused by simply calculating self-attention on learned user representation and cascade representation in previous studies; finally, we conduct comparative experiments on four real datasets, including Twitter and Douban. Experimental results show that the proposed Topic-HGAT model achieves the highest improvements of 2.91% and 1.59% on Hits@100 and MAP@100 indicators, respectively, compared to other 8 baseline models, verifying the rationality and effectiveness of the proposed Topic-HGAT model.en_US
dc.description.sponsorshipHumanities and Social Sciences Research Key Project of Chongqing Municipal Education Commission [23SKGH247]; Chongqing Federation of Social Sciences Key Project [2023NDZD09]; Graduate Innovation Fund of Chongqing University of Technology [gzlcx20223203]en_US
dc.description.sponsorshipThis work is supported in part by Humanities and Social Sciences Research Key Project of Chongqing Municipal Education Commission (23SKGH247) and Chongqing Federation of Social Sciences Key Project (2023NDZD09) , Graduate Innovation Fund of Chongqing University of Technology (gzlcx20223203) .en_US
dc.identifier.doi10.1016/j.jksuci.2023.101852
dc.identifier.issn1319-1578
dc.identifier.issn2213-1248
dc.identifier.issue10en_US
dc.identifier.scopus2-s2.0-85179034086en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org10.1016/j.jksuci.2023.101852
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5272
dc.identifier.volume35en_US
dc.identifier.wosWOS:001130023800001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofJournal of King Saud University-Computer and Information Sciencesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz20240519_kaen_US
dc.subjectInformation Cascadeen_US
dc.subjectDiffusion Predictionen_US
dc.subjectHypergraphen_US
dc.subjectAttention Mechanismen_US
dc.titleA cascade information diffusion prediction model integrating topic features and cross-attentionen_US
dc.typeArticleen_US

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