  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.
