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Entity alignment (EA) aims to find the equivalent entity pairs betweendifferent knowledge graphs (KGs), which is crucial to promote knowledge fusion.With the wide use of temporal knowledge graphs (TKGs), time-aware EA (TEA)methods appear to enhance EA. Existing TEA models are based on Graph NeuralNetworks (GNN) and achieve state-of-the-art (SOTA) performance, but it isdifficult to transfer them to large-scale TKGs due to the scalability issue ofGNN. In this paper, we propose an effective and efficient non-neural EAframework between TKGs, namely LightTEA, which consists of four essentialcomponents: (1) Two-aspect Three-view Label Propagation, (2) Sparse Similaritywith Temporal Constraints, (3) Sinkhorn Operator, and (4) Temporal IterativeLearning. All of these modules work together to improve the performance of EAwhile reducing the time consumption of the model. Extensive experiments onpublic datasets indicate that our proposed model significantly outperforms theSOTA methods for EA between TKGs, and the time consumed by LightTEA is onlydozens of seconds at most, no more than 10% of the most efficient TEA method.

An Effective and Efficient Time-aware Entity Alignment Framework via Two-aspect Three-view Label Propagation