Learning Graph Representation with Generative Adversarial Nets

Hongwei Wang*, Jialin Wang, Jia Wang, Miao Zhao, Weinan Zhang, Fuzheng Zhang, Wenjie Li, Xing Xie, Minyi Guo

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

63 Citations (Scopus)


Graph representation learning aims 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 a graph, and discriminative models that predict the probability of edge between a pair of vertices. In this paper, we propose GraphGAN, an innovative graph representation learning framework unifying the above two classes of methods, in which the generative and the 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, we propose a novel graph softmax as the implementation of the generative model 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 graph reconstruction, link prediction, node classification, recommendation, and visualization, over state-of-The-Art baselines.

Original languageEnglish
Article number8941296
Pages (from-to)3090-3103
Number of pages14
JournalIEEE Transactions on Knowledge and Data Engineering
Issue number8
Publication statusPublished - 1 Aug 2021
Externally publishedYes


  • Graph representation learning
  • generative adversarial nets
  • graph softmax

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