TiRano: Tensorized Relation-aware Temporal Reasoning for Accurate Knowledge Graph Completion

Given a partially observed Temporal Knowledge Graph (TKG), how can we accurately predict missing entities? Unlike static knowledge graphs, TKGs encode facts within temporal contexts, requiring models to reason over both graph structure and time. However, existing TKGC approaches often sample neighbors solely based on temporal proximity, introducing irrelevant context and noise. Moreover, many methods compress snapshots into latent representations and rely on global sequence encoders for temporal modeling, losing edge-level structure and localized relation-specific patterns.