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）任务旨在仅利用少量种子对齐，识别不同知识图谱（KG）中的等价实体。尽管基于聚合的弱监督EA方法已取得显著进展，但其内在机制尚未得到深入探索。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。理论分析表明，这些模型本质上是在为实体对相似度寻找传播算子。我们进一步证明，尽管不同KG存在结构异质性，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，却从未被深入研究。基于这一发现，我们引入一种潜在同构传播算子，以增强跨KG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，通过融入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，并证明PipEA相较于当前最先进的弱监督EA方法取得了显著的性能提升。本研究不仅推动了该领域的发展，也深化了我们对基于聚合的弱监督EA的理解。