  Recently, Graph Convolutional Networks (GCNs) have been widely studied for
graph-structured data representation and learning. However, in many real
applications, data are coming with multiple graphs, and it is non-trivial to
adapt GCNs to deal with data representation with multiple graph structures. One
main challenge for multi-graph representation is how to exploit both structure
information of each individual graph and correlation information across
multiple graphs simultaneously. In this paper, we propose a novel Multiple
Graph Adversarial Learning (MGAL) framework for multi-graph representation and
learning. MGAL aims to learn an optimal structure-invariant and consistent
representation for multiple graphs in a common subspace via a novel adversarial
learning framework, which thus incorporates both structure information of
intra-graph and correlation information of inter-graphs simultaneously. Based
on MGAL, we then provide a unified network for semi-supervised learning task.
Promising experimental results demonstrate the effectiveness of MGAL model.
