Network representation learning based on social similarities

Abstract

Analysis of large-scale networks generally requires mapping high-dimensional network data to a low-dimensional space. We thus need to represent the node and connections accurate and effectively, and representation learning could be a promising method. In this paper, we investigate a novel social similarity-based method for learning network representations. We first introduce neighborhood structural features for representing node identities based on higher-order structural parameters. Then the node representations are learned by a random walk approach that based on the structural features. Our proposed truss2vec is able to maintain both structural similarity of nodes and domain similarity. Extensive experiments have shown that our model outperforms the state-of-the-art solutions.