GraphGAN: Graph Representation Learning with Generative Adversarial Nets

22 Nov 2017Hongwei Wang • Jia Wang • Jialin Wang • Miao Zhao • Weinan Zhang • Fuzheng Zhang • Xing Xie • Minyi Guo

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.

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Evaluation


Task Dataset Model Metric name Metric value Global rank Compare
Node Classification BlogCatalog GraphGAN Accuracy 23.20% # 1
Node Classification BlogCatalog GraphGAN Macro-F1 0.221 # 1
Node Classification Wikipedia GraphGAN Accuracy 21.30% # 1
Node Classification Wikipedia GraphGAN Macro-F1 0.194 # 1