2010/05/26 by Monica G. Campiteli, Mônica G. Campiteli, Adriano J. Holanda +8 · 1 citation
Biochemistry, Genetics and Molecular Biology · Computer Science · Physics and Astronomy · #Bioinformatics and Genomic Networks #Complex Network Analysis Techniques #Computational Drug Discovery Methods #Disordered Systems and Neural Networks (cond-mat.dis-nn) #FOS: Biological sciences #FOS: Physical sciences #Physics and Society (physics.soc-ph) #Quantitative Methods (q-bio.QM) #cond-mat.dis-nn #physics.soc-ph #q-bio.QM
paper · pdf · doi:10.48550/arxiv.1005.4803
8 pages, 4 figures, typos and references corrected, table I corrected
openalex publication_date 2010/05/26 · arxiv created 2010/06/27 · arxiv updated 2010/06/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We study the h Hirsch index as a local node centrality measure for complex networks in general. The h index is compared with the Degree centrality (a local measure), the Betweenness and Eigenvector centralities (two non-local measures) in the case of a biological network (Yeast interaction protein-protein network) and a linguistic network (Moby Thesaurus II) as test environments. In both networks, the Hirsch index has poor correlation with Betweenness centrality but correlates well with Eigenvector centrality, specially for the more important nodes that are relevant for ranking purposes, say in Search Machine Optimization. In the thesaurus network, the h index seems even to outperform the Eigenvector centrality measure as evaluated by simple linguistic criteria.