Social Event Detection (SED) aims to identify significant events from social streams, and has
a wide application ranging from public opinion analysis to risk management. In recent years …

Reliable uncertainty estimation has become a crucial requirement for the industrial
deployment of deep learning algorithms, particularly in high-risk applications such as …

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 …

Real-world social events typically exhibit a severe class-imbalance distribution, which
makes the trained detection model encounter a serious generalization challenge. Most …

State-of-the-art Graph Neural Networks (GNNs) have achieved tremendous success in
social event detection tasks when restricted to a closed set of events. However, considering …

Exploring user-item interaction cues is crucial for the performance of recommender systems.
Explicit investigation of interaction cues is made possible by using graph-based models …

Recently published graph neural networks (GNNs) show promising performance at social
event detection tasks. However, most studies are oriented toward monolingual data in …

The rising popularity of online social network services has attracted a lot of research
focusing on mining various user patterns. Among them, accurate IP geolocation is essential …

Event detection in power systems aims to identify triggers and event types, which helps
relevant personnel respond to emergencies promptly and facilitates the optimization of …

Event extraction (EE) is a fundamental information extraction task that aims to identify
structured events, including event types, triggers and arguments, from unstructured texts …