# Multiple Graph Adversarial Learning

## TL;DR

This paper introduces MGAL, a novel adversarial learning framework that effectively captures both individual graph structures and inter-graph correlations for improved multi-graph data representation.

## Contribution

It proposes a new adversarial learning approach for multi-graph representation that is structure-invariant and captures inter-graph correlations.

## Key findings

- MGAL achieves superior performance on semi-supervised learning tasks.
- Experimental results validate the effectiveness of the proposed framework.
- MGAL successfully learns consistent representations across multiple graphs.

## Abstract

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.