  We investigate the entity alignment problem with unlabeled dangling cases,
meaning that there are entities in the source or target graph having no
counterparts in the other, and those entities remain unlabeled. The problem
arises when the source and target graphs are of different scales, and it is
much cheaper to label the matchable pairs than the dangling entities. To solve
the issue, we propose a novel GNN-based dangling detection and entity alignment
framework. While the two tasks share the same GNN and are trained together, the
detected dangling entities are removed in the alignment. Our framework is
featured by a designed entity and relation attention mechanism for selective
neighborhood aggregation in representation learning, as well as a
positive-unlabeled learning loss for an unbiased estimation of dangling
entities. Experimental results have shown that each component of our design
contributes to the overall alignment performance which is comparable or
superior to baselines, even if the baselines additionally have 30\% of the
dangling entities labeled as training data.
