Similarity-Aware Network Embedding with Self-Paced Learning

Network embedding, which aims to learn low-dimensional vector representations for nodes in a network, has shown promising performance for many real-world applications, such as node classification and clustering. While various embedding methods have been developed for network data, they are limited in their assumption that nodes are correlated with their neighboring nodes with the same similarity degree. As such, these methods can be suboptimal for embedding network data. In this paper, we propose a new method named SANE, short for Similarity-Aware Network Embedding, to learn node representations by explicitly considering different similarity degrees between connected nodes in a network. In particular, we develop a new framework based on self-paced learning by accounting for both the explicit relations (i.e., observed links) and implicit relations (i.e., unobserved node similarities) in network representation learning. To justify our proposed model, we perform experiments on two real-world network data. Experiments results show that SNAE outperforms state-of-the-art embedding models on the tasks of node classification and node clustering.