Temporal-Weighted Transfer Network for Knowledge Graph Extrapolation
Temporal Knowledge Graph (TKG) reasoning aims to predict future events by analyzing historical snapshots across different timestamps, making it an important research direction. However, most existing methods rely either on probabilistic statistical models or on historical propagation mechanisms in isolation, without the ability to simultaneously capture both. This limitation makes it difficult to fully model dependencies between related and unrelated events, while also overlooking time-sensitive features and structural relationships across timestamps. To address these challenges, we propose the Temporal-Weighted Transfer Network (TWTNET), a novel reasoning model that jointly leverages statistical modeling and historical information transfer. TWTNET is composed of two modules: the Temporal Memory Module, which applies a copy-based scoring mechanism to capture event dependencies and emphasize critical entities, and the Dynamic Temporal Information Exchange Module, which learns entity evolution patterns and dynamically integrates temporal information into embeddings. By simultaneously capturing probabilistic regularities and historical dependencies, TWTNET achieves more robust and interpretable extrapolative reasoning. Experiments on five benchmark datasets demonstrate that TWTNET consistently outperforms state-of-the-art models, confirming its effectiveness in TKG reasoning tasks.