TSGAN: Temporal Social Graph Attention Network for Aggressive Behavior Forecasting

摘要

The propagation of aggressive behavior in online social networks presents a growing threat to digital well-being and social harmony. While existing research focuses on modeling aggression diffusion or detecting aggressive content, forecasting individual user aggression remains an open challenge. This work fills this gap by introducing Temporal Social Graph Attention Network (TSGAN), a social-aware sequence-to-sequence architecture designed to forecast aggressive behavior in dynamic social networks. The core of TSGAN is an adaptive socio-temporal attention module that dynamically models social influence and temporal dynamics. To capture global social influence, TSGAN employs a graph contrastive learning approach to generate global network context embeddings. TSGAN utilizes an aggression intensity metric derived from a proposed hybrid aggression content detection model (92.87% F1), combining a fine-tuned transformer with a large language model to quantify user aggression over time. TSGAN uniquely addresses user inactivity, models dynamic follower relationship impacts, and accounts for temporal behavioral decay while scaling to large networks. Experiments on real-world datasets (X for aggression forecasting and Flickr for popularity prediction) demonstrate TSGAN’s versatility and effectiveness. TSGAN outperforms baselines in forecasting across hourly, daily, and weekly temporal intervals, showing up to 24.8% improvement in daily aggression predictions.