Subgraph Centrality in Complex Networks

TL;DR

This paper introduces subgraph centrality, a new network measure based on spectral properties that better identifies important nodes and motifs in complex networks compared to traditional centrality metrics.

Contribution

The paper presents a novel spectral-based centrality measure that emphasizes participation in smaller subgraphs, improving node ranking and motif detection in complex networks.

Findings

Subgraph centrality effectively ranks nodes in real-world networks.

SC correlates more strongly with node lethality than degree centrality.

SC reveals scale-free properties in biological networks.

Abstract

We introduce a new centrality measure that characterizes the participation of each node in all subgraphs in a network. Smaller subgraphs are given more weight than larger ones, which makes this measure appropriate for characterizing network motifs. We show that the subgraph centrality (SC) can be obtained mathematically from the spectra of the adjacency matrix of the network. This measure is better able to discriminate the nodes of a network than alternate measures such as degree, closeness, betweenness and eigenvector centralities. We study eight real-world networks for which SC displays useful and desirable properties, such as clear ranking of nodes and scale-free characteristics. Compared with the number of links per node, the ranking introduced by SC (for the nodes in the protein interaction network of S. cereviciae) is more highly correlated with the lethality of individual proteins removed from the proteome.