What Makes Entities Similar? A Similarity Flooding Perspective for
  Multi-sourced Knowledge Graph Embeddings
Zequn Sun Jiacheng Huang Xiaozhou Xu Qijin Chen Weijun Ren Wei Hu
Joint representation learning over multi-sourced knowledge graphs (KGs)
yields transferable and expressive embeddings that improve downstream tasks.
Entity alignment (EA) is a critical step in this process. Despite recent
considerable research progress in embedding-based EA, how it works remains to
be explored. In this paper, we provide a similarity flooding perspective to
explain existing translation-based and aggregation-based EA models. We prove
that the embedding learning process of these models actually seeks a fixpoint
of pairwise similarities between entities. We also provide experimental
evidence to support our theoretical analysis. We propose two simple but
effective methods inspired by the fixpoint computation in similarity flooding,
and demonstrate their effectiveness on benchmark datasets. Our work bridges the
gap between recent embedding-based models and the conventional similarity
flooding algorithm. It would improve our understanding of and increase our
faith in embedding-based EA.