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arXiv · 1901.07439

Multiple Graph Adversarial Learning

Bo Jiang·Ziyan Zhang·Jin Tang·Bin Luo
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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.

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Bo Jiang, Ziyan Zhang, Jin Tang, Bin Luo. 2019-01-22. Multiple Graph Adversarial Learning. https://arxiv.org/abs/1901.07439

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