Year: 2025
Type: conference-paper
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.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4410637003