Deterministic Modularity Optimization

Abstract

We study community structure of networks. We have developed a scheme for maximizing the modularity Q based on mean field methods. Further, we have defined a simple family of random networks with community structure; we understand the behavior of these networks analytically. Using these networks, we show how the mean field methods display better performance than previously known deterministic methods for optimization of Q.

7 pages, 4 figures, minor changes

References in corpus (15)

• Statistical mechanics of complex networks
• The structure and function of complex networks
• Finding and evaluating community structure in networks
• Modularity and community structure in networks
• Fast algorithm for detecting community structure in networks
• Uncovering the overlapping community structure of complex networks in nature and society
• Finding community structure in networks using the eigenvectors of matrices
• Functional cartography of complex metabolic networks
• Evolution of networks
• Resolution limit in community detection
• Comparing community structure identification
• Statistical Mechanics of Community Detection
• Modularity from Fluctuations in Random Graphs and Complex Networks
• Classes of complex networks defined by role-to-role connectivity profiles
• Eprints and the Open Archives Initiative

Cited by in corpus (8)

• Community detection in graphs
• A smart local moving algorithm for large-scale modularity-based community detection
• Improved community structure detection using a modified fine tuning strategy
• Measuring Significance of Community Structure in Complex Networks
• Multi-resolution community detection based on generalized self-loop rescaling strategy
• Optimizing an Organized Modularity Measure for Topographic Graph Clustering: a Deterministic Annealing Approach
• Enhancing community detection by local structural information
• Community detection based on significance optimization in complex networks