LDSNE: Learning structural network embeddings by encoding local distances

Abstract

Network embedding algorithms learn low-dimensional features from the relationships and attributes of networks. The basic principle of these algorithms is to preserve the similarities in the original networks as much as possible. However, existing algorithms are not expressive enough for structural identity similarities. Therefore, we propose LDSNE, a novel algorithm for learning structural representations in both directed and undirected networks. Networks are first mapped into a proximity-based low-dimension space. Then, structural embeddings are extracted by encoding local space distances. Empirical results demonstrate that our algorithm can obtain multiple types of representations and outperforms other state-of-the-art methods.