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

TL;DR

This paper introduces a method to identify and analyze unique singular nodes in complex networks using local features and multivariate statistics, revealing their functional roles across various real-world systems.

Contribution

It extends the principle of deviations from the average to systematically detect and characterize singular motifs in complex networks using a novel multivariate approach.

Findings

Singular motifs have distinct functional roles in networks.

Real-world networks show more diverse singular motifs than benchmark models.

Barabási-Albert model's singular nodes correspond to hubs.

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 Barab\'asi-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.