Abstract
Pervasive rumors in social networks have significantly harmed society due to their seditious and misleading effects. Existing rumor detection studies only consider practical features from a propagation tree, but ignore the important differences and potential relationships of subtrees under the same propagation tree. To address this limitation, we propose a novel heterogeneous propagation graph model to capture the relevance among different propagation subtrees, named Multi-subtree Heterogeneous Propagation Graph Attention Network (MHGAT). Specifically, we implicitly fuse potential relationships among propagation subtrees using the following three methods: 1) We leverage the structural logic of a tree to construct different types of propagation subtrees in order to distinguish the differences among multiple propagation subtrees; 2) We construct a heterogeneous propagation graph based on such differences, and design edge weights of the graph according to the similarity of propagation subtrees; 3) We design a propagation subtree interaction scheme to enhance local and global information exchange, and finally, get the high-level representation of rumors. Extensive experimental results on three real-world datasets show that our model outperforms the most advanced method.
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Li, G., Hu, J., Wu, Y., Zhang, X., Zhou, W., Lyu, H. (2023). A Heterogeneous Propagation Graph Model for Rumor Detection Under the Relationship Among Multiple Propagation Subtrees. In: Amini, MR., Canu, S., Fischer, A., Guns, T., Kralj Novak, P., Tsoumakas, G. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2022. Lecture Notes in Computer Science(), vol 13714. Springer, Cham. https://doi.org/10.1007/978-3-031-26390-3_13
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