Self-Paced Network Embedding

Network embedding has attracted increasing attention in recent data mining research with many real-world applications. Network embedding is to learn low-dimensional representations for nodes in a network. A popular kind of existing methods, such as DeepWalk, Node2Vec, and LINE, learn node representations by pushing positive context node to the anchor node while pushing negative context nodes away from it in the low-dimensional vector space. When sampling the negative context nodes, they usually employ a predefined sampling distribution based on the node popularity. However, this sampling distribution often fails to capture the real informativeness of each node and cannot reflect the training state. To address these important problems, in this paper, we propose a novel self-paced network embedding method. Specifically, our method can adaptively capture the informativeness of each node based on the current training state, and sample negative context nodes in terms of their informativeness. The proposed self-paced sampling strategy can gradually select difficult negative context nodes with training process going on to learn better node representations. Moreover, to better capture the node informativeness for learning node representations, we extend our method to the generative adversarial network framework, which has the larger capacity to discover node informativeness. The extensive experiments have been conducted on the benchmark network datasets to validate the effectiveness of our proposed methods.