Motif-Aware Adversarial Graph Representation Learning

Ming Zhao,Xing Xu,Xuewen Xia,Yinglong Zhang

doi:10.1109/access.2022.3144233

Ming Zhao, Xing Xu + Show 2 more

Open Access

https://doi.org/10.1109/access.2022.3144233

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Abstract

Graph representation learning has been extensively studied in recent years. It has been proven effective in network analysis and mining tasks such as node classification and link prediction. Learning method based on neural network has become mainstream solution because it can efficiently preserve nonlinear characteristics of graph. However, more efficient ways to encode complex graph structures are still being explored. In this paper, for undirected graphs, we present a novel Motif-Aware Generative Adversarial Network (MotifGAN) model to learn graph representation based on a re-weighted graph, which unifies both the Motif higher order structure and the original lower order structure. Firstly, we obtain the motif adjacency matrix by motif mining, which encodes the Motif higher order structure. Then we couple the motif adjacency matrix with the adjacency matrix of the original graph to get a reweighted matrix named motif connectivity matrix. The motif connectivity distribution implicit in the motif connectivity matrix is the target structure information we need to preserve. How to preserve a wealth of structural information is a challenge. Inspired by related applications of GAN, we formulate a GAN model to generate embedding representations that satisfy the target structure conditions. The GAN model usually consists of two components: generator and discriminator. Our generator tries to approximate the motif connectivity distribution, while the discriminator detects whether the connectivity of two vertexes is from ground truth or generated by the generator. In adversarial training, both parts can alternately and iteratively boost their performance. Experimental results on public datasets show that MotifGAN has made substantial progress in various applications, including link prediction, node classification and visualization.

Highlights

Graph is an important form of data that can naturally encode a wealth of information
Zhao et al.: Motif-Aware Adversarial Graph Representation Learning by fitting the connection distribution, ProGAN [15] generated proximity based on Generative Adversarial Network (GAN) and learned the embedding representation that preserves the proximity by an encoder, A-RNE [16] focused on sampling high-quality negative vertices to achieve a better similarity ranking among vertices pairs, and CANE [17] designed a novel adversarial learning framework to capture the network communities
3) We conduct experiments with real-world graphs, and the results demonstrate that MotifGAN outperforms other state-of-the-art methods in link prediction, node classification, and visualization tasks

Summary

INTRODUCTION

Graph is an important form of data that can naturally encode a wealth of information. As one of the important ways to process graph data, graph representation learning is known as network embedding. Many studies have successfully introduced the GAN model into graph representation learning, such as GraphGAN [14] captured the network structure. M. Zhao et al.: Motif-Aware Adversarial Graph Representation Learning by fitting the connection distribution, ProGAN [15] generated proximity based on GAN and learned the embedding representation that preserves the proximity by an encoder, A-RNE [16] focused on sampling high-quality negative vertices to achieve a better similarity ranking among vertices pairs, and CANE [17] designed a novel adversarial learning framework to capture the network communities. For preserve a wealth of motif connectivity information, we design a GAN framework to generate high-quality samples that approximated closely to the motif connectivity distribution. Our contributions are mainly threefold: 1) We design a novel Motif higher order structure and construct a motif connectivity matrix by unifying the Motif higher order structure and the original lower order structure. 2) We formulate and train a GAN model to approximate the motif connectivity distribution and generate the desired graph representation. 3) We conduct experiments with real-world graphs, and the results demonstrate that MotifGAN outperforms other state-of-the-art methods in link prediction, node classification, and visualization tasks

RELATED WORK

GAN STRUCTURE

EXPERIMENTS

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CONCLUSION