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Leonardo D. H. Soares

Publications and source records attributed to Leonardo D. H. Soares.

2 recordsLinked to original sources

A simple centrality index for scientific social recognition

We introduce a new centrality index for bipartite network of papers and authors that we call $K$-index. The $K$-index grows with the citation performance of the papers that cite a given researcher and can seen as a measure of scientific social recognition. Indeed, the $K$-index measures the number of hubs, defined in a self-consistent way in the bipartite network, that cites a given author. We show that the $K$-index can be computed by simple inspection of the Web of Science platform and presents several advantages over other centrality indexes, in particular Hirsch $h$-index. The $K$-index is robust to self-citations, is not limited by the total number of papers published by a researcher as occurs for the $h$-index and can distinguish in a consistent way researchers that have the same $h$-index but very different scientific social recognition. The $K$-index easily detects a known case of a researcher with inflated number of papers, citations and $h$-index due to scientific misconduct. Finally, we show that, in a sample of twenty-eight physics Nobel laureates and twenty-eight highly cited non-Nobel-laureate physicists, the $K$-index correlates better to the achievement of the prize than the number of papers, citations, citations per paper, citing articles or the $h$-index. Clustering researchers in a $K$ versus $h$ plot reveals interesting outliers that suggest that these two indexes can present complementary independent information.

cs.DL↗

Lobby index as a network centrality measure

We study the lobby index (l-index for short) as a local node centrality measure for complex networks. The l-inde is compared with degree (a local measure), betweenness and Eigenvector centralities (two global measures) in the case of biological network (Yeast interaction protein-protein network) and a linguistic network (Moby Thesaurus II). In both networks, the l-index has poor correlation with betweenness but correlates with degree and Eigenvector. Being a local measure, one can take advantage by using the l-index because it carries more information about its neighbors when compared with degree centrality, indeed it requires less time to compute when compared with Eigenvector centrality. Results suggests that l-index produces better results than degree and Eigenvector measures for ranking purposes, becoming suitable as a tool to perform this task.

cs.SI↗