Correction: Identifying and Characterizing Nodes Important to Community Structure Using the Spectrum of the Graph
Year: 2011
Type: erratum
Abstract: Background: Many complex systems can be represented as networks, and how a network breaks up into subnetworks or communities is of wide interest.However, the development of a method to detect nodes important to communities that is both fast and accurate is a very challenging and open problem. Methodology/Principal Findings:In this manuscript, we introduce a new approach to characterize the node importance to communities.First, a centrality metric is proposed to measure the importance of network nodes to community structure using the spectrum of the adjacency matrix.We define the node importance to communities as the relative change in the eigenvalues of the network adjacency matrix upon their removal.Second, we also propose an index to distinguish two kinds of important nodes in communities, i.e., ''community core'' and ''bridge''.Conclusions/Significance: Our indices are only relied on the spectrum of the graph matrix.They are applied in many artificial networks as well as many real-world networks.This new methodology gives us a basic approach to solve this challenging problem and provides a realistic result.
Source: PLoS ONE
Authors: Yang Wang, Zengru Di, Ying Fan
Institutions: Semmelweis University, Beijing Normal University
Language: English
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,382
OpenAlex ID: W4251417264