  We study dangling-aware entity alignment in knowledge graphs (KGs), which is
an underexplored but important problem. As different KGs are naturally
constructed by different sets of entities, a KG commonly contains some dangling
entities that cannot find counterparts in other KGs. Therefore, dangling-aware
entity alignment is more realistic than the conventional entity alignment where
prior studies simply ignore dangling entities. We propose a framework using
mixed high-order proximities on dangling-aware entity alignment. Our framework
utilizes both the local high-order proximity in a nearest neighbor subgraph and
the global high-order proximity in an embedding space for both dangling
detection and entity alignment. Extensive experiments with two evaluation
settings shows that our framework more precisely detects dangling entities, and
better aligns matchable entities. Further investigations demonstrate that our
framework can mitigate the hubness problem on dangling-aware entity alignment.
