Enhancing Temporal Link Prediction with HierTKG: A Hierarchical Temporal Knowledge Graph Framework
This addresses misinformation control for public decision-making during crises, offering a scalable solution for real-time rumor prediction.
The paper tackles the problem of misinformation spread on social media by proposing HierTKG, a framework that combines Temporal Graph Networks and hierarchical pooling to model rumor dynamics across temporal and structural scales, achieving MRR scores of 0.9845 on ICEWS14 and 0.9312 on WikiData.
The rapid spread of misinformation on social media, especially during crises, challenges public decision-making. To address this, we propose HierTKG, a framework combining Temporal Graph Networks (TGN) and hierarchical pooling (DiffPool) to model rumor dynamics across temporal and structural scales. HierTKG captures key propagation phases, enabling improved temporal link prediction and actionable insights for misinformation control. Experiments demonstrate its effectiveness, achieving an MRR of 0.9845 on ICEWS14 and 0.9312 on WikiData, with competitive performance on noisy datasets like PHEME (MRR: 0.8802). By modeling structured event sequences and dynamic social interactions, HierTKG adapts to diverse propagation patterns, offering a scalable and robust solution for real-time analysis and prediction of rumor spread, aiding proactive intervention strategies.