Weakly Supervised Entity Alignment (EA) is the task of identifying equivalent entities across diverse knowledge graphs (KGs) using only a limited number of seed alignments. Despite substantial advances in aggregation-based weakly supervised EA, the underlying mechanisms in this setting remain unexplored. In this paper, we present a propagation perspective to analyze weakly supervised EA and explain the existing aggregation-based EA models. Our theoretical analysis reveals that these models essentially seek propagation operators for pairwise entity similarities. We further prove that, despite the structural heterogeneity of different KGs, the potentially aligned entities within aggregation-based EA models have isomorphic subgraphs, which is the core premise of EA but has not been investigated. Leveraging this insight, we introduce a potential isomorphism propagation operator to enhance the propagation of neighborhood information across KGs. We develop a general EA framework, PipEA, incorporating this operator to improve the accuracy of every type of aggregation-based model without altering the learning process. Extensive experiments substantiate our theoretical findings and demonstrate PipEA's significant performance gains over state-of-the-art weakly supervised EA methods. Our work not only advances the field but also enhances our comprehension of aggregation-based weakly supervised EA.

翻译：弱监督实体对齐（EA）是在仅使用有限种子对齐的情况下，跨不同知识图谱（KG）识别等价实体的任务。尽管基于聚合的弱监督EA取得了显著进展，但该设置下的底层机制仍未得到探索。本文提出一种传播视角来分析弱监督EA，并解释现有基于聚合的EA模型。我们的理论分析表明，这些模型本质上是在寻找成对实体相似性的传播算子。我们进一步证明，尽管不同知识图谱的结构存在异质性，基于聚合的EA模型中潜在对齐的实体具有同构子图——这是EA的核心前提但此前未被研究。利用这一见解，我们引入一种潜在同构传播算子，以增强跨知识图谱的邻域信息传播。我们开发了一个通用EA框架PipEA，该框架在不改变学习过程的前提下，通过集成该算子提升了各类基于聚合模型的准确性。大量实验验证了我们的理论发现，并展示了PipEA相较于最先进的弱监督EA方法在性能上的显著提升。我们的工作不仅推动了该领域的发展，还加深了对基于聚合的弱监督EA的理解。