Learning flexible network representation via anonymous walks

Abstract

Network representation learning aims to project nodes in a network into low-dimensional continuous vector space while preserving the network structure and inherent node properties. Most of the existing approaches focus merely on the local context of nodes but ignore the local structural patterns of nodes that are important in network analysis tasks. A flexible network representation learning algorithm that can be generalized across a variety of domains and tasks should conform to two principles: to learn representations where nodes sharing similar local contexts have similar vectors and to embed nodes sharing similar local structures closely together. In this paper, we propose a flexible framework that incorporates local structural information into a generic model capturing contextual information. Specifically, anonymous walks are exploited to capture local structural patterns. In addition, we design two strategies to incorporate local structural patterns into the basic continuous bag-of-words (CBOW) architecture through statistic-based structural similarity and embedding-based structural vectors. Extensive experiments on real-world network data sets indicate that the proposed models incorporating local structural patterns outperform seven state-of-the-art network representation learning models in various tasks.