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Resolution limit in community detection.

Santo Fortunato¹, Marc Barthélemy

¹School of Informatics and Center for Biocomplexity, Indiana University, Bloomington, IN 47406, USA.

Proceedings of the National Academy of Sciences of the United States of America

|December 28, 2006

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Modularity optimization, a common network analysis technique, may miss smaller communities. This limitation affects various network types, necessitating checks for reliable community detection.

Area of Science:

Network Science
Computational Social Science
Bioinformatics

Background:

Community structure detection is crucial for understanding complex networks across disciplines.
Modularity optimization is a widely adopted method for identifying network communities.
Assessing the reliability of community detection methods is essential for accurate analysis.

Purpose of the Study:

To investigate the limitations of modularity optimization in detecting small network communities.
To determine the factors influencing the failure of modularity optimization to resolve smaller modules.
To provide guidance on assessing the reliability of community detection results.

Main Methods:

Analysis of modularity optimization performance on artificial and real-world networks.

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Evaluation of module resolution limits based on network size and inter-module connectivity.

Development of criteria for assessing the reliability of detected communities.

Main Results:

Modularity optimization may fail to identify communities smaller than a network-dependent scale.
This limitation is observed across diverse network types, including social, biological, and technological networks.
A significant number of modules are often unresolved by modularity optimization.

Conclusions:

Modularity optimization has inherent limitations in resolving small-scale community structures.
A critical assessment of community detection results obtained via modularity optimization is necessary.
The study provides foundational elements for evaluating the robustness of this community detection approach.