EIAN: Explicit Interaction-aware Attention Network for Interpretable Event Modeling

Event sequences are integral to domains such as e-commerce, social networks, and healthcare. Traditional point process models, like Poisson and Hawkes processes, are foundational but limited by rigid parametric assumptions, constraining their flexibility in complex real-world scenarios. Neural point processes offer a more adaptable alternative, but typically perform implicit sequence modeling, which does not fully exploit critical event interaction patterns and limits transparency. To address these challenges, we introduce the Explicit Interaction-aware Attention Network (EIAN), a novel model that enhances event modeling by explicitly capturing both intra-type and cross-type event interactions. Specifically, EIAN employs two key components: an intra-type temporal encoder that preserves the unique temporal dynamics within each event type, and a cross-type interaction decoder that highlights interactions across event types. Furthermore, two temporal encoding mechanisms are integrated into the interaction decoder to handle irregular inter-event intervals in diverse temporal scenarios. Extensive experiments show that EIAN consistently outperforms existing models in predictive performance and provides deeper insights into event interaction patterns, advancing both flexibility and interpretability. Our code is available at https://github.com/CGCL-codes/EIAN.git.