Enhanced Social Event Detection through Dynamically Weighted Meta-Paths Modeling
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(Published version)
Date
2025
Authors
Ma, C.
Qiu, Z.
Wang, H.
Du, J.
Xue, S.
Wu, J.
Yang, J.
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Conference paper
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Companion Proceedings of the ACM Web Conference (WWW 2025), 2025, pp.1184-1188
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Congbo Ma, Zitai Qiu, Hu Wang, Jing Du, Shan Xue, Jia Wu, Jian Yang
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The ACM Web Conference (WWW) (28 Apr 2025 - 2 May 2025 : Sydney, Australia)
Abstract
Social event detection (SED) identifies significant events in social networks by analyzing complex interactions using both textual data and structural relationships that often span multiple nodes. This requires exploring long-range dependencies, which increases computational costs, especially with many neighbors. Therefore, in this paper, we present the DynamicallyWeighted Meta-Paths Modeling (DWMM) framework for large-scale social event detection. It includes three main modules: 1) a graph building module to convert social event data into Heterogeneous Information Networks (HINs); 2) a meta-path searching module to determine the significant meta-paths and their importance; 3) a model training module that uses weighted top-k meta-paths for social event detection. Extensive experiments on three widely used social event detection datasets show that DWMM enhances performance, demonstrating its effectiveness.
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© 2025 Copyright held by the owner/author(s). This work is licensed under a Creative Commons Attribution International 4.0 License.