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      Hirsch index as a network centrality measure

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          Abstract

          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.

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          Most cited references 11

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          Centrality and network flow

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            Comparison of the Hirsch-index with standard bibliometric indicators and with peer judgment for 147 chemistry research groups

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              Does the h-index have predictive power?

               J. E. Hirsch (2007)
              Bibliometric measures of individual scientific achievement are of particular interest if they can be used to predict future achievement. Here we report results of an empirical study of the predictive power of the h-index compared to other indicators. Our findings indicate that the h-index is better than other indicators considered (total citation count, citations per paper, and total paper count) in predicting future scientific achievement. We discuss reasons for the superiority of the h-index.
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                Author and article information

                Journal
                26 May 2010
                2010-06-27
                1005.4803

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

                Custom metadata
                8 pages, 4 figures, typos and references corrected, table I corrected
                physics.soc-ph cond-mat.dis-nn q-bio.QM

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