2009/11/05 by Kristina Lerman, Lerman, Kristina, Rumi Ghosh +1
Biochemistry, Genetics and Molecular Biology · Physics and Astronomy · #Bioinformatics and Genomic Networks #Complex Network Analysis Techniques #FOS: Physical sciences #Physics and Society (physics.soc-ph) #physics.soc-ph
paper · pdf · doi:10.48550/arxiv.0911.1095
4 pages, 2 figures
arxiv created 2009/11/05 · openalex publication_date 2009/11/05 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Bonacich centrality measures the number of attenuated paths between nodes in a network. We use this metric to study network structure, specifically, to rank nodes and find community structure of the network. To this end we extend the modularity-maximization method for community detection to use this centrality metric as a measure of node connectivity. Bonacich centrality contains a tunable parameter that sets the length scale of interactions. By studying how rankings and discovered communities change when this parameter is varied allows us to identify globally important nodes and structures. We apply the proposed method to several benchmark networks and show that it leads to better insight into network structure than earlier methods.