Introduces flow adjacency matrices and defines influence and betweenness measures (strength, power, domain, diameter) to quantify node, edge, and subsystem importance in complex network flow models.
Improved spectral algorithm for the detection of network communities
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abstract
We review and improve a recently introduced method for the detection of communities in complex networks. This method combines spectral properties of some matrices encoding the network topology, with well known hierarchical clustering techniques, and the use of the modularity parameter to quantify the goodness of any possible community subdivision. This provides one of the best available methods for the detection of community structures in complex systems.
fields
physics.soc-ph 1years
2019 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
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Influence and Betweenness in Flow Models of Complex Network Systems
Introduces flow adjacency matrices and defines influence and betweenness measures (strength, power, domain, diameter) to quantify node, edge, and subsystem importance in complex network flow models.