Multiple Graph Adversarial Learning
This addresses the challenge of multi-graph representation for applications with complex data structures, but it appears incremental as it builds on existing GCN methods.
The paper tackles the problem of representing data with multiple graph structures by proposing a Multiple Graph Adversarial Learning (MGAL) framework, which learns structure-invariant and consistent representations in a common subspace, achieving promising experimental results.
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.