Fast unfolding of communities in large networks

Abstract

We propose a simple method to extract the community structure of large networks. Our method is a heuristic method that is based on modularity optimization. It is shown to outperform all other known community detection method in terms of computation time. Moreover, the quality of the communities detected is very good, as measured by the so-called modularity. This is shown first by identifying language communities in a Belgian mobile phone network of 2.6 million customers and by analyzing a web graph of 118 million nodes and more than one billion links. The accuracy of our algorithm is also verified on ad-hoc modular networks. .

6 pages, 5 figures, 1 table; new version with new figures in order to clarify our method, where we look more carefully at the role played by the ordering of the nodes and where we compare our method with that of Wakita and Tsurumi

References in corpus (8)

• Uncovering the overlapping community structure of complex networks in nature and society
• Near linear time algorithm to detect community structures in large-scale networks
• Comparing community structure identification
• Quantifying social group evolution
• Geographical dispersal of mobile communication networks
• Analysis of the structure of complex networks at different resolution levels
• Size reduction of complex networks preserving modularity
• Majority Model on a network with communities

Cited by in corpus (10)

• Line Graphs, Link Partitions and Overlapping Communities
• Community detection in networks with positive and negative links
• Towards real-time community detection in large networks
• Communities, Knowledge Creation, and Information Diffusion
• Quantifying and identifying the overlapping community structure in networks
• Spectral tripartitioning of networks
• Divide and Conquer: Partitioning Online Social Networks
• Consistent Community Identification in Complex Networks
• Multi-level algorithms for modularity clustering
• Impact of Random Failures and Attacks on Poisson and Power-Law Random Networks