Improving community detection in networks by targeted node removal

How a network breaks up into subnetworks or communities is of wide interest. Here we show that vertices connected to many other vertices across a network can disturb the community structures of otherwise ordered networks, introducing noise. We investigate strategies to identify and remove noisy vert...

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Bibliographic Details
Published in:Physical review. E, Statistical, nonlinear, and soft matter physics Statistical, nonlinear, and soft matter physics, 2011-01, Vol.83 (1 Pt 2), p.016114-016114, Article 016114
Main Authors: Wen, Haoran, Leicht, E A, D'Souza, Raissa M
Format: Article
Language:English
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Summary:How a network breaks up into subnetworks or communities is of wide interest. Here we show that vertices connected to many other vertices across a network can disturb the community structures of otherwise ordered networks, introducing noise. We investigate strategies to identify and remove noisy vertices ("violators") and develop a quantitative approach using statistical breakpoints to identify when the largest enhancement to a modularity measure is achieved. We show that removing nodes thus identified reduces noise in detected community structures for a range of different types of real networks in software systems and in biological systems.
ISSN:1539-3755
1550-2376
DOI:10.1103/PhysRevE.83.016114