科研之友
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摘要
Graph generative adversarial network has achieved remarkable effectiveness, such as link prediction, node classification, user recommendation and node visualization in recent years. Most existing methods mainly focus on how to represent the proximity between nodes according to the structure of the graph. However, the graph nodes also have rich attribute information in social networks, the traditional methods mainly consider the node attributes as auxiliary information incorporate into the embedding representation of the graph to improve the accuracy of node classification and link prediction. In fact, in social networks, these node attributes are often sparse. Due to privacy and other reasons, the attributes of many nodes are difficult to obtain. Inspired by the application of generative adversarial network in image field, we propose an innovative framework to discover node latent attribute. Through experiments, we demonstrate the effectiveness of our proposed methods.
