用双曲图卷积+注意力机制捕捉事件多阶关系，提升社交事件检测效果。
Multi-Order Hyperbolic Graph Convolution and Aggregated Attention for Social Event Detection
- 基于双曲空间建模事件层级结构，融合多阶关系信息
- 在多个数据集上显著优于现有方法，提升检测准确率
- 适合处理具有层次结构的社交事件分析任务
社交事件检测（SED）旨在识别现实世界中的具体事件，在社交媒体平台如Twitter、Weibo和Facebook中具有广泛应用。该任务有助于企业洞察用户偏好，支持公共部门应对突发事件与灾害管理。由于事件数据具有层级特性，传统欧氏空间方法难以充分捕捉其复杂关系。尽管已有研究在欧氏与双曲空间取得进展，但普遍忽略事件间的多阶关联。为此，本文提出新型框架MOHGCAA（多阶双曲图卷积与聚合注意力），通过双曲空间建模与多阶关系聚合，增强事件表征能力。实验表明，在有监督与无监督设置下均实现显著性能提升。在多个数据集上的广泛评估验证了该框架的有效性与鲁棒性，能有效应对社交事件检测中的常见挑战。
原文摘要 · Abstract (English)
Social event detection (SED) is a task focused on identifying specific real-world events and has broad applications across various domains. It is integral to many mobile applications with social features, including major platforms like Twitter, Weibo, and Facebook. By enabling the analysis of social events, SED provides valuable insights for businesses to understand consumer preferences and supports public services in handling emergencies and disaster management. Due to the hierarchical structure of event detection data, traditional approaches in Euclidean space often fall short in capturing the complexity of such relationships. While existing methods in both Euclidean and hyperbolic spaces have shown promising results, they tend to overlook multi-order relationships between events. To address these limitations, this paper introduces a novel framework, Multi-Order Hyperbolic Graph Convolution with Aggregated Attention (MOHGCAA), designed to enhance the performance of SED. Experimental results demonstrate significant improvements under both supervised and unsupervised settings. To further validate the effectiveness and robustness of the proposed framework, we conducted extensive evaluations across multiple datasets, confirming its superiority in tackling common challenges in social event detection.
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