Subgraph centrality and clustering in complex hyper-networks

By Ernesto Estrada, Juan Alberto Rodríguez-Velázquez. Physica A: Statistical Mechanics and its Applications, 2005

314 citations recorded by the source corpus.

The representation of complex systems as networks is inappropriate for the study of certain problems. We show several examples of social, biological, ecological and technological systems where the use of complex networks gives very limited information about the structure of the system. Consequently, we extend the concepts of subgraph centrality and clustering for complex networks represented by hypergraphs: complex hyper-networks. The first parameter characterizes the node participation in different sub-hypergraphs and the second one characterizes the transitivity in the hyper-network through the proportion of hyper-triangles to paths of length two. Another measure characterizing the formation of triples of mutually adjacent groups in the hyper-network is also introduced. All of these characteristics are studied in three different hyper-networks: a scientific collaboration hyper-network, an ecological competition hyper-network and the hyper-network formed by the American corporate elite in 1999.

Subfields: Experimental and Cognitive Psychology, Molecular Biology, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/584e985e7017d26b3da57e43264bb320aaab55ec

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Experimental and Cognitive Psychology

1. J. Rodríguez
2. Ernesto Estrada

In Molecular Biology

1. J. Rodríguez
2. Ernesto Estrada
3. Ivan Gutman
4. Michele Benzi
5. Nicholas John Higham

In Statistical and Nonlinear Physics

1. J. Rodríguez
2. Ernesto Estrada
3. Michele Benzi
4. Naomichi Hatano
5. Nicholas John Higham

Citations in the corpus

• Linear and Multilinear Algebra (2007, methodology): “Both subgraph centralization and even centralization, were used to obtain measures of bipartivity in graphs [9]: the proportion of even closed-walks to the total number of closed walks is a measure of the graph bipartivity.”
• Hypergraphs and Cellular Networks (2009, unknown): “. Another proposal for clustering coefficients in hypergraphs can be found in”