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.

该论文提出了 GraphGAN，一种结合生成模型和判别模型的图形表示学习框架，其中两个模型玩一个博弈论最小最大化游戏来提高它们的性能，同时还提出了一种新的图形 softmax 来克服传统 softmax 函数的局限性。在真实世界的数据集上广泛实验表明，GraphGAN 在各种应用中都比现有技术取得了实质性的收益，包括链接预测、节点分类和推荐。