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Graph alignment, also known as network alignment, is a fundamental task insocial network analysis. Many recent works have relied on partially labeledcross-graph node correspondences, i.e., anchor links. However, due to theprivacy and security issue, the manual labeling of anchor links for diversescenarios may be prohibitive. Aligning two graphs without any anchor links is acrucial and challenging task. In this paper, we propose an UnsupervisedAdversarial Graph Alignment (UAGA) framework to learn a cross-graph alignmentbetween two embedding spaces of different graphs in a fully unsupervisedfashion (\emph{i.e.,} no existing anchor links and no users' personal profileor attribute information is available). The proposed framework learns theembedding spaces of each graph, and then attempts to align the two spaces viaadversarial training, followed by a refinement procedure. We further extend ourUAGA method to incremental UAGA (iUAGA) that iteratively reveals the unobserveduser links based on the pseudo anchor links. This can be used to furtherimprove both the embedding quality and the alignment accuracy. Moreover, theproposed methods will benefit some real-world applications, \emph{e.g.,} linkprediction in social networks. Comprehensive experiments on real-world datademonstrate the effectiveness of our proposed approaches UAGA and iUAGA forunsupervised graph alignment.

Unsupervised Adversarial Graph Alignment with Graph Embedding