Efficient Community Detection in Large Scale Networks

Abstract

One of the most important features of a network is its division into communities, groups of nodes with many internal and few external connections. Furthermore, the community structure of a network can be organized hierarchically, which reflects a natural behavior of real life phenomena. It is a difficult task to detect and understand the community structure of a network and it becomes even more challenging as data availability (and networks sizes) increases. This work presents a efficient implementation for community detection in networks aiming on modularity maximization based on the Newman's spectral method with a fine tuning(FT) stage. This work presents a modification on the FT which substantially reduces the execution time, while preserving the division quality. A high performance implementation of the method enables their application to large real world networks. The Newman's spectral method can be applied to networks with more than 1 million nodes in a personal computer.