A Meta-Strategy Enhancement for Network Embedding

Abstract

Network embedding, which learns continuous low-dimension representations of nodes, provides an effective way for many network analysis tasks, such as node classification and link prediction. Most existing models are time-consuming and cannot be applied to dynamic networks. To alleviate these issues, we apply Arora's sentence2vec model to network embedding to enhance the performance of existing network embedding methods. Under the same framework of the sentence2vec, we name the network embedding method MNE^2, which allows us to leverage the latent representation obtained from a given embedding approach to learn enhanced node embeddings in a network. Taking into account interactions between nodes in the network, the enhanced network embedding can be viewed as the latent factors for generating embedding representations of neighbor nodes. Then, PCA is applied to modify the embedding results to make the enhanced embeddings more expressive. We evaluate MNE^2 on three real-world social network datasets for node classification and link prediction tasks. The results show that MNE^2 can outperform state-of-the-art network embedding learning methods in both tasks.