Network Embedding via Link Strength Adjusted Random Walk

Abstract

Network embedding is a useful tool to map graph structures into vector spaces, which facilitates graph analysis tasks including node classification, graph visualization, similarity calculation etc. Existing network embedding methods calculate embedding vectors based on node series generated by random walks. These methods treat all the links equally during the random walk procedure, which leads to the missing of structural information that is key to the embedding performance. We therefore propose in this paper a novel random walk-based network embedding method called Self-Adjusting Random Walk (SARW). SARW utilizes a self-adjusting strategy that makes the walking biased towards the links that are more strongly connected in order to better capture the structural information. Further more, the strengths of links are updated using the embedding output as feedback. Through experiments we have verified that our method out performs state-of-the-art network embedding methods, in node classification tasks and link prediction tasks.