MGARL: Multiple Graph Adversarial Regularized Learning

Ziyan Zhang, Bo Jiang, Bin Luo

Abstract: Graph Convolutional Networks (GCNs) have been commonly studied for graph learning tasks, such as semi-supervised learning, clustering etc. However, many existing GCNs are generally conducted on single graph data and thus can not be applied directly to multi-graph data that consists of various types of edges between nodes. To address this issue, in this paper, we propose a novel multiple Graph Adversarial Regularized Learning (mGARL) framework for multi-graph data representation and learning. mGARL aims to learn an optimal structure invariant/consistent representation for multiple graphs by employing a novel Encoder-Decoder architecture with adversarial learning regularization. It can incorporate the structural information of multiple graphs simultaneously for the node’s representation. We apply the proposed mGARL on the multi-view semi-supervised learning tasks. Experimental results on several datasets demonstrate the effectiveness and benefits of the proposed mGARL model.