SPEA: Large-Scale Entity Alignment via Self-Partitioning

The task of entity alignment (EA) seeks to identify corresponding entities across different knowledge graphs (KGs). However, in large-scale KG alignment tasks, the complexity of the problem renders traditional entity structure representation methods, designed for small-scale KGs, ineffective. Partition-based approaches address this challenge by breaking large KGs into smaller subgraphs, but this inevitably results in a loss of structural information. Although existing methods have sought to mitigate this issue, they have largely overlooked the interplay between partitioning and entity structure representation learning. To address this, we propose a Self-Partitioning Entity Alignment (SPEA) pipeline for large-scale EA, in which partitioning and entity structure representation learning are mutually optimized. Within this framework, we introduce the Inter-Subgraph Neighbor Interaction (ISNI) for enhanced entity structure representation, the Bidirectional Margin-based Confidence (BMC) for pseudo-pairing, the Seed-oriented Cross Graph Partitioner (SCGP) for dynamic repartitioning, and the Historical Confidence Ensembling (HCE) strategy for consistent training. Extensive experiments demonstrate that SPEA significantly outperforms existing methods for large-scale EA tasks.