Betweenness centrality correlation in social networks.

By Kwang-Il Goh, Eunsoon Oh, Byungnam Kahng, D. Kim. Physical review. E, Statistical, nonlinear, and soft matter physics, 2002

299 citations recorded by the source corpus.

Scale-free (SF) networks exhibiting a power-law degree distribution can be grouped into the assortative, dissortative, and neutral networks according to the behavior of the degree-degree correlation coefficient. Here we investigate the betweenness centrality (BC) correlation for each type of SF networks. While the BC-BC correlation coefficients behave similarly to the degree-degree correlation coefficients for the dissortative and neutral networks, the BC correlation is nontrivial for the assortative ones found mainly in social networks. The mean BC of neighbors of a vertex with BC g(i) is almost independent of g(i), implying that each person is surrounded by almost the same influential environments of people no matter how influential the person may be.

Subfields: Geometry and Topology, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/06810ffd14aed07c374413a419d906beaa150053

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In Geometry and Topology

In Statistical and Nonlinear Physics

Citations in the corpus

Graph theory and networks in Biology. (2006, background): “The relationship between betweenness centrality and node degree in scale-free networks was investigated in [104, 105] in which it was shown that the relationship between betweenness centrality (BC) and node-degree approximately follows the rule BC ∼ k(γ−1)/(η−1).”
A Survey on Trust Modeling (2015, background): “There are abundant metrics in the literature to model centrality or inﬂuence in networks [Everett and Borgatti 2005; Bonacich 1987; Brandes 2001; Freeman 1979; Freeman et al. 1991; Goh et al. 2003; Sabidussi 1966; Hage and Harary 1995; Shimbel 1953].”
The Importance of Bottlenecks in Protein Networks: Correlation with Gene Essentiality and Expression Dynamics (2007, background): “As discussed above, previous studies have shown that, in biological networks, hubs tend to be essential [7,9], and betweenness of a node is correlated with its degree [20].”
Coauthorship networks and patterns of scientific collaboration (2004, background): “[35] have extended their investigation of betweenness to the correlation between the betweenness scores of collaborators.”
The rich-club phenomenon in the Internet topology (2003, background): “tion [1] [2] [3] (degree is the number of links a node has) and they are known as scale-free networks. Scale-free networks can be grouped into assortative, disassortative and neutral networks [4] [5] [6]. Social networks (e.g. the co-authorship network) are scale-free assortative networks, in which ”
Scientific collaboration and endorsement: Network analysis of coauthorship and citation networks (2011, background): “Later, Goh et al. (2003) found that authors with high betweenness centralities do not prefer to collaborate with the same sort of authors. Newman (2004) found that scientists who work as a team tend to have shorter average distances to other scientists in the graph.”