Deep convolutional adversarial graph autoencoder using positive pointwise mutual information for graph embedding

Graph embedding aims to map the high-dimensional nodes to a low-dimensional space and learns the graph relationship from its latent representations. Most existing graph embedding methods focus on the topological structure of graph data, but ignore the semantic information of graph data, which results in the unsatisfied performance in practical applications. To overcome the problem, this paper proposes a novel deep convolutional adversarial graph autoencoder ( GAE) model. To embed the semantic information between nodes in the graph data, the random walk strategy is first used to construct the positive pointwise mutual information ( PPMI) matrix, then, graph convolutional net-work ( GCN) is employed to encode the PPMI matrix and node content into the latent representation. Finally, the learned latent representation is used to reconstruct the topological structure of the graph data by decoder. Furthermore, the deep convolutional adversarial training algorithm is introduced to make the learned latent representation conform to the prior distribution better. The state-of-the-art experimental results on the graph data validate the effectiveness of the proposed model in the link prediction, node clustering and graph visualization tasks for three standard datasets, Cora, Citeseer and Pubmed.