title: Improving community detection in networks by targeted node removal.
abstract: 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.
authors.affiliation: Department of Mechanical and Aerospace Engineering, University of California, Davis, California 95616, USA.
authors.forename: Haoran
authors.initials: H
authors.lastname: Wen
authors.forename: E A
authors.initials: EA
authors.lastname: Leicht
authors.forename: Raissa M
authors.initials: RM
authors.lastname: D'Souza