  Graph representation learning aims to encode all nodes of a graph into
low-dimensional vectors that will serve as input of many compute vision tasks.
However, most existing algorithms ignore the existence of inherent data
distribution and even noises. This may significantly increase the phenomenon of
over-fitting and deteriorate the testing accuracy. In this paper, we propose a
Distribution-induced Bidirectional Generative Adversarial Network (named DBGAN)
for graph representation learning. Instead of the widely used normal
distribution assumption, the prior distribution of latent representation in our
DBGAN is estimated in a structure-aware way, which implicitly bridges the graph
and feature spaces by prototype learning. Thus discriminative and robust
representations are generated for all nodes. Furthermore, to improve their
generalization ability while preserving representation ability, the
sample-level and distribution-level consistency is well balanced via a
bidirectional adversarial learning framework. An extensive group of experiments
are then carefully designed and presented, demonstrating that our DBGAN obtains
remarkably more favorable trade-off between representation and robustness, and
meanwhile is dimension-efficient, over currently available alternatives in
various tasks.
