Multi-granularity Complex Network Representation Learning

Network representation learning aims to learn the low dimensional vector of the nodes in a network while maintaining the inherent properties of the original information. Existing algorithms focus on the single coarse-grained topology of nodes or text information alone, which cannot describe complex information networks. However, node structure and attribution are interdependent, indecomposable. Therefore, it is essential to learn the representation of node based on both the topological structure and node additional attributes. In this paper, we propose a multi-granularity complex network representation learning model (MNRL), which integrates topological structure and additional information at the same time, and presents these fused information learning into the same granularity semantic space that through fine-to-coarse to refine the complex network. Experiments show that our method can not only capture indecomposable multi-granularity information, but also retain various potential similarities of both topology and node attributes. It has achieved effective results in the downstream work of node classification and the link prediction on real-world datasets.