Unsupervised Graph Representation by Periphery and Hierarchical Information Maximization

• URL: http://arxiv.org/abs/2006.04696v1
• Date: Mon, 8 Jun 2020 15:50:40 GMT
• Title: Unsupervised Graph Representation by Periphery and Hierarchical Information Maximization
• Authors: Sambaran Bandyopadhyay, Manasvi Aggarwal, M. Narasimha Murty
• Abstract summary: Invent of graph neural networks has improved the state-of-the-art for both node and the entire graph representation in a vector space. For the entire graph representation, most of existing graph neural networks are trained on a graph classification loss in a supervised way. We propose an unsupervised graph neural network to generate a vector representation of an entire graph in this paper.
• Score: 18.7475578342125
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep representation learning on non-Euclidean data types, such as graphs, has gained significant attention in recent years. Invent of graph neural networks has improved the state-of-the-art for both node and the entire graph representation in a vector space. However, for the entire graph representation, most of the existing graph neural networks are trained on a graph classification loss in a supervised way. But obtaining labels of a large number of graphs is expensive for real world applications. Thus, we aim to propose an unsupervised graph neural network to generate a vector representation of an entire graph in this paper. For this purpose, we combine the idea of hierarchical graph neural networks and mutual information maximization into a single framework. We also propose and use the concept of periphery representation of a graph and show its usefulness in the proposed algorithm which is referred as GraPHmax. We conduct thorough experiments on several real-world graph datasets and compare the performance of GraPHmax with a diverse set of both supervised and unsupervised baseline algorithms. Experimental results show that we are able to improve the state-of-the-art for multiple graph level tasks on several real-world datasets, while remain competitive on the others.

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