As a trending approach for social event detection, graph neural network (GNN)-based methods enable a fusion of natural language semantics and the complex social network structural information, thus showing SOTA performance. However, GNN-based methods can miss useful message correlations. Moreover, they require manual labeling for training and predetermining the number of events for prediction. In this work, we address social event detection via graph structural entropy (SE) minimization. While keeping the merits of the GNN-based methods, the proposed framework, HISEvent, constructs more informative message graphs, is unsupervised, and does not require the number of events given a priori. Specifically, we incrementally explore the graph neighborhoods using 1-dimensional (1D) SE minimization to supplement the existing message graph with edges between semantically related messages. We then detect events from the message graph by hierarchically minimizing 2-dimensional (2D) SE. Our proposed 1D and 2D SE minimization algorithms are customized for social event detection and effectively tackle the efficiency problem of the existing SE minimization algorithms. Extensive experiments show that HISEvent consistently outperforms GNN-based methods and achieves the new SOTA for social event detection under both closed- and open-set settings while being efficient and robust.

本文介绍了一种名为HISEvent的框架，通过构建信息更丰富的消息图，在保留图神经网络方法优点的同时，无监督地进行社交事件检测，并克服了现有结构熵最小化算法的效率问题。实验证明，HISEvent在封闭集和开放集环境下持续优于基于图神经网络的方法，在高效和稳健性方面取得了最新的SOTA社交事件检测结果。

层次化和增量化的结构熵最小化用于无监督社会事件检测