General Structure Preserving Network Embedding

Abstract

Network embedding has attracted increasing attention in recent years since it represents large scale networks in low-dimensional space and provides an easier way to analysis networks. Existing embedding methods either focus on preserving the microscopic topology structure, or incorporate the mesoscopic community structure of a network. However, in the real world, a network may not only contain community structure, but have bipartite-structure, star-structure or other general structures, where nodes in each cluster have similar patterns of connections to other nodes. Empirically, general structure is important for describing the features of networks. In this paper, based on nonnegative matrix factorization framework, we propose GS-NMF which is capable of integrating topology structure and general structure into embedding process. The experimental results show that GS-NMF overcomes the limitation of previous methods and achieves obvious improvement on node clustering, node classification, and visualization.