Optima Network Science Seminar
Faculty Advisor: My T. Thai
Coordinator: Thang N. Dinh
Time and place: 1:50pm Wed, E520A CSE Building
Note:
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interested in participating in the activities of the seminar and/or wish to
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upcoming events please send an email to tdinh@cise.ufl.edu.
by Abdul Alim on Jan 23, 2013
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.
Identifying and Characterizing Nodes Important to Community Structure Using the Spectrum of the Graph
by Yang Wang, Zengru Di, Ying Fan, PLoS ONE 6(11) [PDF][Slide]
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.