Consistent Community Identification in Complex Networks

2009/10/08 by Haewoon Kwak, Kwak, Haewoon, Young-Ho Eom +7
Physics and Astronomy · Psychology · #Complex Network Analysis Techniques #Data Analysis #FOS: Physical sciences #Mental Health Research Topics #Opinion Dynamics and Social Influence #Physics and Society (physics.soc-ph) #Statistics and Probability (physics.data-an) #physics.data-an #physics.soc-ph

paper · pdf · doi:10.48550/arxiv.0910.1508

4 pages, 4 figures

openalex publication_date 2009/10/08 · arxiv created 2009/10/10 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We have found that known community identification algorithms produce inconsistent communities when the node ordering changes at input. We propose two metrics to quantify the level of consistency across multiple runs of an algorithm: pairwise membership probability and consistency. Based on these two metrics, we address the consistency problem without compromising the modularity. Our solution uses pairwise membership probabilities as link weights and generates consistent communities within six or fewer cycles. It offers a new tool in the study of community structures and their evolutions.

Citations

• Finding Community Structure in Mega-scale Social Networks
• Uncovering the overlapping community structure of complex networks in nature and society

Related

• Constant Communities in Complex Networks
• A Flexible Fitness Function for Community Detection in Complex Networks
• Identification of Community Structures in Networks Employing a Modified Divisive Algorithm
• A New Metric for Quality of Network Community Structure
• Enhancing Stability and Assessing Uncertainty in Community Detection through a Consensus-based Approach