Community detection based on local topological information and its application in power grid

Abstract

Community structure detection algorithm is employed in a vast amount of study to partition a network into some loosely coupled sub-networks of smaller scale. It is an effective tool to analyze and control some large-scale networks such as power grids. This paper proposes a novel algorithm based on local similarity to detect the community structure in complex network. Firstly, a new similarity index between nodes is defined to model the topological closeness of local connections in networks. Then nodes sharing high similarity are gathered to form the community structure. The results suggest the emergence of the bridging nodes and kernels within the community detection process. This proposed method performs well when it is introduced to seek out the actual community structure, kernels and bridging nodes in some benchmark networks. Thirdly, the proposed algorithm lends itself to many applications, such as detecting communities in several IEEE standard power grids and investigating the roles of bridging nodes in cascading failures. Experimentally, the proposed algorithm outperforms some other similarity indices and clustering methods. Finally, a detailed comparison helps us get the conclusion that the traditional label propagation algorithm is a special case of the proposed algorithm.