Beyond the average: Detecting global singular nodes from local features in complex networks

¹  Instituto de Física de São Carlos, Universidade de São Paulo - São Carlos, SP, PO Box 369, 13560-970 Brazil
²  Jacobs University Bremen, School of Engineering and Science - Campus Ring 6, 28759 Bremen, Germany, EU
³  Boston University - Sargent College, Department of Health Sciences - 635 Commonwealth Ave, Boston, MA 02215, USA
⁴  School of Computing Science, Newcastle University - Claremont Tower, Newcastle upon Tyne, NE1 7RU, UK, EU
⁵  Institute of Neuroscience, Newcastle University - Framlington Place, Newcastle upon Tyne, NE2 4HH, UK, EU
⁶  Department of Brain and Cognitive Sciences, Seoul National University, College of Natural Sciences Shilim, Gwanak, Seoul 151-747, Korea

Corresponding author: luciano@if.sc.usp.br

Received: 8 April 2009
Accepted: 16 June 2009

Abstract

Deviations from the average can provide valuable insights about the organization of natural systems. The present article extends this important principle to the systematic identification and analysis of singular motifs in complex networks. Six measurements quantifying different and complementary features of the connectivity around each node of a network were calculated, and multivariate statistical methods applied to identify singular nodes. The potential of the presented concepts and methodology was illustrated with respect to different types of complex real-world networks, namely the US air transportation network, the protein-protein interactions of the yeast Saccharomyces cerevisiae and the Roget thesaurus networks. The obtained singular motifs possessed unique functional roles in the networks. Three classic theoretical network models were also investigated, with the Barabási-Albert model resulting in singular motifs corresponding to hubs, confirming the potential of the approach. Interestingly, the number of different types of singular node motifs as well as the number of their instances were found to be considerably higher in the real-world networks than in any of the benchmark networks.