How a network breaks up into sub-networks 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.
Wen et al. 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 REF .
Improving community detection in networks by targeted node removal
Physical review. E, Statistical, nonlinear, and soft matter physics
Physical review. E, Statistical, nonlinear, and soft matter physics
Physics Medicine