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CHE-TKG is introduced as a collaborative dual-view learning framework for temporal knowledge graph reasoning, explicitly modeling historical evidence and evolutionary dynamics. It constructs separate graphs for each view, using dedicated encoders and relation decomposition to capture predictive signals. Experiments show CHE-TKG achieves state-of-the-art performance on multiple benchmarks by effectively leveraging the complementary information.
State-of-the-art temporal knowledge graph reasoning is now possible by jointly modeling historical evidence and evolutionary dynamics, unlocking complementary predictive signals.
Temporal knowledge graph (TKG) reasoning aims to predict future events from historical facts. A key challenge lies in jointly capturing two sources of predictive information in TKGs: historical evidence and evolutionary dynamics. However, existing methods typically focus on only one of these sources, which limits the ability to fully exploit the complementary predictive signals in TKGs. To address this, we propose CHE-TKG, a novel collaborative dual-view learning framework for TKG reasoning. CHE-TKG explicitly separates and jointly models historical evidence and evolutionary dynamics, aiming to learn and exploit their complementary predictive signals. Specifically, CHE-TKG constructs a historical evidence graph to capture long-term structural regularities and stable relational constraints, alongside an evolutionary dynamics graph to model temporal transitions and recent changes, with dedicated encoders for each view. We further employ relation decomposition and a contrastive alignment objective to better capture the predictive signals across the two views. Extensive experiments demonstrate that CHE-TKG achieves state-of-the-art performance on multiple benchmarks.
