Integrating User Relation Representations and Information Diffusion Topology Features for Information Propagation Prediction

doi:10.3778/j.issn.1673-9418.2309050

Abstract

Abstract: Information propagation prediction aims to analyze the patterns of information spreading in social networks and social media, thus understand and predict the information diffusion process. Recent researches have shown that information propagation is influenced by both group and individual relations. Existing works have mainly concentrated on group relations, including social relation and dynamic structural relation. They ignore a core group relation, i.e., the co-occurrence user relation, and an important individual relation, i.e., the user-preference relation, leading to incomplete modeling of the information propagation process. To address this issue, this paper comprehensively considers both group and individual relations, and proposes an information propagation prediction model that integrates user relation representations and information diffusion topology features. For the group relation, this paper constructs a user co-occurrence graph to learn user similarity relation representations, which are then fused with information diffusion topology features to capture group relations. For the individual relation, this paper fuses user representation and influencing factors to capture internal factors on stimulating users to share information. Experimental results show that the performance of the proposed model on two public datasets is improved, and the MAP@k and hits@k evaluation indicators on the Memetracker dataset are improved by an average of 6.54% and 2.75%, respectively.

Key words: information diffusion prediction, topological structure, similarity, common interest

摘要： 信息传播预测旨在分析信息传播网络或社交媒体中信息扩散的规律，以理解和预测信息的传播过程。现有工作主要关注用户群体关系中的社交关系和动态影响关系，忽略了群体关系中用户间相似性关系和个体关系中内在因素对用户转发信息的影响。为此，提出了融合用户关系表示和信息传播拓扑特征的信息传播预测模型，从群体关系和个体关系两个层面分析用户受信息影响的可能性。在群体关系层面，构建用户共现次数图来学习用户相似性关系表示，随后融合了用户关系表示和信息传播拓扑特征来更全面地捕捉群体关系；在个体关系层面，融合了用户个体特征表示和影响因素向量，以捕捉不同的内在因素对刺激用户转发信息的影响。实验结果表明，该模型在两个公开数据集上性能均有提升，在Memetracker数据集上[MAP@k]和[hits@k]评价指标分别平均提升了6.54%和2.75%。

关键词: 信息传播预测, 拓扑结构, 相似性, 共同兴趣

WU Yunbing, GAO Hang, ZENG Weisen, YIN Aiying. Integrating User Relation Representations and Information Diffusion Topology Features for Information Propagation Prediction[J]. Journal of Frontiers of Computer Science and Technology, 2024, 18(10): 2738-2749.

吴运兵, 高航, 曾炜森, 阴爱英. 融合用户关系表示和信息传播拓扑特征的信息传播预测[J]. 计算机科学与探索, 2024, 18(10): 2738-2749.

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