  Recent years have witnessed a flurry of research activity in graph matching,
which aims at finding the correspondence of nodes across two graphs and lies at
the heart of many artificial intelligence applications. However, matching
heterogeneous graphs with partial overlap remains a challenging problem in
real-world applications. This paper proposes the first practical
learning-to-match method to meet this challenge. The proposed unsupervised
method adopts a novel partial OT paradigm to learn a transport plan and node
embeddings simultaneously. In a from-one-to-all manner, the entire learning
procedure is decomposed into a series of easy-to-solve sub-procedures, each of
which only handles the alignment of a single type of nodes. A mechanism for
searching the transport mass is also proposed. Experimental results demonstrate
that the proposed method outperforms state-of-the-art graph matching methods.
