Broad information diffusion modelling for sharing link click prediction using knowledge graphs
Author Identifier
Jianxin Li: https://orcid.org/0000-0002-9059-330X
Document Type
Journal Article
Publication Title
Expert Systems with Applications
Volume
277
Publisher
Elsevier
School
School of Business and Law
Publication Unique Identifier
10.1016/j.eswa.2025.127276
RAS ID
79350
Funders
National Natural Science Foundation of China (62476247, 62072409) / “Pioneer” and “Leading Goose” R&D Program of Zhejiang (2024C01214) / Zhejiang Provincial Natural Science Foundation (LR21F020003)
Abstract
In the new media era, users actively share and diffuse information across social networks, creating complex patterns of broad information diffusion (BID) that differ significantly from traditional recommendation scenarios. Existing models are primarily designed for deep information diffusion (DID) with sequential cascades and struggle to address BID challenges, including the sparse graph structure, weak temporal correlation, and ambiguity in user preferences. To bridge this gap, we propose K-BID, a knowledge-driven framework tailored for BID scenarios. K-BID integrates semantic and social graph information through a two-phase ‘Match & Rank’ approach. The matching phase retrieves candidate voters using social relationships and personalized preferences, whereas the ranking phase refines predictions by modelling temporal dynamics. Experiments on real-world datasets demonstrate the superiority of K-BID over state-of-the-art methods, achieving significant improvements of 14.02%, 16.80%, and 16.99% in Precision, MRR, and AUC respectively, for the ‘Soc.’ objective with K=5. Our work advances the understanding of BID scenarios and offers a practical solution for optimizing information dissemination in social platforms.
DOI
10.1016/j.eswa.2025.127276
Access Rights
subscription content
Comments
Kong, X., Shu, C., Wang, L., Zhou, H., Zhu, L., & Li, J. (2025). Broad information diffusion modelling for sharing link click prediction using knowledge graphs. Expert Systems with Applications, 277, 127276. https://doi.org/10.1016/j.eswa.2025.127276