  We consider the general setting for partial matching of two or multiple
graphs, in the sense that not necessarily all the nodes in one graph can find
their correspondences in another graph and vice versa. We take a universe
matching perspective to this ubiquitous problem, whereby each node is either
matched into an anchor in a virtual universe graph or regarded as an outlier.
Such a universe matching scheme enjoys a few important merits, which have not
been adopted in existing learning-based graph matching (GM) literature. First,
the subtle logic for inlier matching and outlier detection can be clearly
modeled, which is otherwise less convenient to handle in the pairwise matching
scheme. Second, it enables end-to-end learning especially for universe level
affinity metric learning for inliers matching, and loss design for gathering
outliers together. Third, the resulting matching model can easily handle new
arriving graphs under online matching, or even the graphs coming from different
categories of the training set. To our best knowledge, this is the first deep
learning network that can cope with two-graph matching, multiple-graph
matching, online matching, and mixture graph matching simultaneously. Extensive
experimental results show the state-of-the-art performance of our method in
these settings.
