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.
|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|