  Graph embedding is an effective method to represent graph data in a low
dimensional space for graph analytics. Most existing embedding algorithms
typically focus on preserving the topological structure or minimizing the
reconstruction errors of graph data, but they have mostly ignored the data
distribution of the latent codes from the graphs, which often results in
inferior embedding in real-world graph data. In this paper, we propose a novel
adversarial graph embedding framework for graph data. The framework encodes the
topological structure and node content in a graph to a compact representation,
on which a decoder is trained to reconstruct the graph structure. Furthermore,
the latent representation is enforced to match a prior distribution via an
adversarial training scheme. To learn a robust embedding, two variants of
adversarial approaches, adversarially regularized graph autoencoder (ARGA) and
adversarially regularized variational graph autoencoder (ARVGA), are developed.
Experimental studies on real-world graphs validate our design and demonstrate
that our algorithms outperform baselines by a wide margin in link prediction,
graph clustering, and graph visualization tasks.
