Enhancing Temporal Link Prediction with HierTKG: A Hierarchical Temporal Knowledge Graph Framework
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Decision MakingLink PredictionMisinformationSimilar Papers 제목 키워드 기반
UniHR: Hierarchical Representation Learning for Unified Knowledge Graph Link Prediction
Beyond-triple fact representations including hyper-relational facts with auxiliary key-value pairs, temporal facts with additional timestamps, and nested facts implying relationships between facts, are gaining significan…
Link PredictionRepresentation LearningDiscrete-time Temporal Network Embedding via Implicit Hierarchical Learning in Hyperbolic Space
Representation learning over temporal networks has drawn considerable attention in recent years. Efforts are mainly focused on modeling structural dependencies and temporal evolving regularities in Euclidean space which,…
Graph EmbeddingGraph Neural NetworkLink PredictionNetwork Embedding+1HGWaveNet: A Hyperbolic Graph Neural Network for Temporal Link Prediction
Temporal link prediction, aiming to predict future edges between paired nodes in a dynamic graph, is of vital importance in diverse applications. However, existing methods are mainly built upon uniform Euclidean space, w…
Graph Neural NetworkLink PredictionHow to Bridge Spatial and Temporal Heterogeneity in Link Prediction? A Contrastive Method
Temporal Heterogeneous Networks play a crucial role in capturing the dynamics and heterogeneity inherent in various real-world complex systems, rendering them a noteworthy research avenue for link prediction. However, ex…
Contrastive LearningLink PredictionTrajectory-User Linking via Hierarchical Spatio-Temporal Attention Networks
Trajectory-User Linking (TUL) is crucial for human mobility modeling by linking diferent trajectories to users with the exploration of complex mobility patterns. Existing works mainly rely on the recurrent neural framewo…