Hierarchical and Incremental Structural Entropy Minimization for
  Unsupervised Social Event Detection
Yuwei Cao Hao Peng Zhengtao Yu Philip S. Yu
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.