Understanding and Guiding Weakly Supervised Entity Alignment with
  Potential Isomorphism Propagation
Yuanyi Wang Wei Tang Haifeng Sun Zirui Zhuang Xiaoyuan Fu Jingyu Wang Qi Qi Jianxin Liao
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.