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Entity alignment (EA) aims to find the equivalent entities in different KGs,which is a crucial step in integrating multiple KGs. However, most existing EAmethods have poor scalability and are unable to cope with large-scale datasets.We summarize three issues leading to such high time-space complexity inexisting EA methods: (1) Inefficient graph encoders, (2) Dilemma of negativesampling, and (3) "Catastrophic forgetting" in semi-supervised learning. Toaddress these challenges, we propose a novel EA method with three newcomponents to enable high Performance, high Scalability, and high Robustness(PSR): (1) Simplified graph encoder with relational graph sampling, (2)Symmetric negative-free alignment loss, and (3) Incremental semi-supervisedlearning. Furthermore, we conduct detailed experiments on several publicdatasets to examine the effectiveness and efficiency of our proposed method.The experimental results show that PSR not only surpasses the previous SOTA inperformance but also has impressive scalability and robustness.

Are Negative Samples Necessary in Entity Alignment? An Approach with High Performance, Scalability and Robustness