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Versatility of nodal affiliation to communities
Maxwell Shinn1, Rafael Romero-Garcia2, Jakob Seidlitz2,3
1Department of Psychiatry, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, CB2 0SZ, United Kingdom. maxwell.shinn@yale.edu.
Scientific Reports
|June 29, 2017
Summary
We introduce versatility, a new metric for network analysis, to quantify how ambiguously nodes belong to communities. Lower versatility indicates clear community assignment, aiding in network decomposition.
Area of Science:
- Network science
- Graph theory
- Computational neuroscience
Background:
- Community detection in networks is crucial for understanding structure.
- Node affiliation to single communities is often ambiguous, posing a challenge for analysis.
- Existing methods struggle with this inherent ambiguity in network modularity.
Purpose of the Study:
- To introduce a novel metric, versatility (V), to quantify nodal affiliation ambiguity in networks.
- To demonstrate versatility's utility in conjunction with existing community detection algorithms.
- To improve the resolution of community structure decomposition in complex networks.
Main Methods:
- Developed versatility (V) as a metric where V≈0 indicates consistent assignment and V>>0 indicates inconsistent assignment.
- Applied versatility in conjunction with hierarchical community detection algorithms.
- Evaluated versatility on idealized networks and real-world networks like the mouse brain connectome.
Main Results:
- Versatility satisfies desirable theoretical properties for quantifying modular decomposition ambiguity.
- Local minima of global mean versatility identified optimal resolution parameters for community detection.
- Successfully decomposed community structure in a social network and the mouse brain connectome with reduced ambiguity.
Conclusions:
- Nodal versatility is a valuable metric for quantifying the inherent ambiguity in network community structure.
- Versatility aids in selecting optimal parameters for community detection algorithms.
- This metric enhances the understanding of modular decomposition in complex systems.
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