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Home > Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 32

GraphGAN: Graph Representation Learning With Generative Adversarial Nets

March 15, 2023

Authors

Hongwei Wang

Shanghai Jiao Tong University


Jia Wang

The Hong Kong Polytechnic University


Jialin Wang

Huazhong University of Science and Technology


Miao Zhao

The Hong Kong Polytechnic University


Weinan Zhang

Shanghai Jiao Tong University


Fuzheng Zhang

Microsoft Research Asia


Xing Xie

Microsoft Research Asia


Minyi Guo

Shanghai Jiao Tong University


Published:

2018-02-08

Proceedings:

Proceedings of the AAAI Conference on Artificial Intelligence, 32

Volume

Issue:

Thirty-Second AAAI Conference on Artificial Intelligence 2018

Track:

Main Track: Machine Learning Applications

Downloads:

Download PDF

Abstract:

The goal of graph representation learning is to embed each vertex in a graph into a low-dimensional vector space. Existing graph representation learning methods can be classified into two categories: generative models that learn the underlying connectivity distribution in the graph, and discriminative models that predict the probability of edge existence between a pair of vertices. In this paper, we propose GraphGAN, an innovative graph representation learning framework unifying above two classes of methods, in which the generative model and discriminative model play a game-theoretical minimax game. Specifically, for a given vertex, the generative model tries to fit its underlying true connectivity distribution over all other vertices and produces "fake" samples to fool the discriminative model, while the discriminative model tries to detect whether the sampled vertex is from ground truth or generated by the generative model. With the competition between these two models, both of them can alternately and iteratively boost their performance. Moreover, when considering the implementation of generative model, we propose a novel graph softmax to overcome the limitations of traditional softmax function, which can be proven satisfying desirable properties of normalization, graph structure awareness, and computational efficiency. Through extensive experiments on real-world datasets, we demonstrate that GraphGAN achieves substantial gains in a variety of applications, including link prediction, node classification, and recommendation, over state-of-the-art baselines.

DOI:

10.1609/aaai.v32i1.11872


AAAI

Thirty-Second AAAI Conference on Artificial Intelligence 2018


ISSN 2374-3468 (Online) ISSN 2159-5399 (Print)


Published by AAAI Press, Palo Alto, California USA Copyright © 2018, Association for the Advancement of Artificial Intelligence All Rights Reserved.

Topics: AAAI

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