Loading...
Enhanced Social Event Detection through Dynamically Weighted Meta-Paths Modeling
Ma, Congbo Qiu, Zitai Wang, Hu Du, Jing Xue, Shan Wu, Jia Yang, Jian
Ma, Congbo
Qiu, Zitai
Wang, Hu
Du, Jing
Xue, Shan
Wu, Jia
Yang, Jian
Supervisor
Department
Computer Vision
Embargo End Date
Type
Conference proceeding
Date
2025
License
Language
English
Collections
Research Projects
Organizational Units
Journal Issue
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 Dynamically Weighted 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. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.
Citation
C. Ma et al., “Enhanced Social Event Detection through Dynamically Weighted Meta-Paths Modeling,” vol. 5, no. 25, pp. 1184–1188, May 2025, doi: 10.1145/3701716.3715480
Source
WWW Companion 2025 - Companion Proceedings of the ACM Web Conference 2025
Conference
34th ACM Web Conference, WWW Companion 2025
Keywords
Data Mining, Deep Learning, Heterogeneous Graph, Social Event Detection
Subjects
Source
34th ACM Web Conference, WWW Companion 2025
Publisher
Association for Computing Machinery