Semantic-aware network embedding via optimized random walk and paragaraph2vec

摘要

The Deepwalk-based network embedding algorithm, which was inspired by the word2vec model, can quickly and efficiently learn a low-dimensional vector representation for each node in a complex network. However, the success of word2vec in natural language environments relies heavily on ubiquitous contextual semantics, and the Deepwalk model is less accurate than other deep models due to ignoring contextual semantics in complex net-works. To solve this challenge, a semantic-aware network embedding algorithm is proposed in this paper, simply called SANE, via optimized random walk and paragaraph2vec. In the SANE, a semantic-aware random walk strategy is designed to unsupervised generate many semantic-bearing node sequences from a complex network as a training set. Then, based on the training set, the paragraph2vec model is optimized by considering both global semantics and local semantics for learning more accurate low-dimensional node feature representations. Ex-periments on multiple tasks and multiple networks show the advantages of our SANE algorithm.