GraphGAN: Graph Representation Learning With Generative Adversarial Nets
GraphGAN: Graph Representation Learning With Generative Adversarial Nets
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 …