  Network alignment, in general, seeks to discover the hidden underlying
correspondence between nodes across two (or more) networks when given their
network structure. However, most existing network alignment methods have added
assumptions of additional constraints to guide the alignment, such as having a
set of seed node-node correspondences across the networks or the existence of
side-information. Instead, we seek to develop a general network alignment
algorithm that makes no additional assumptions. Recently, network embedding has
proven effective in many network analysis tasks, but embeddings of different
networks are not aligned. Thus, we present our Deep Adversarial Network
Alignment (DANA) framework that first uses deep adversarial learning to
discover complex mappings for aligning the embedding distributions of the two
networks. Then, using our learned mapping functions, DANA performs an efficient
nearest neighbor node alignment. We perform experiments on real world datasets
to show the effectiveness of our framework for first aligning the graph
embedding distributions and then discovering node alignments that outperform
existing methods.
