Nodal statistics-based equivalence relation for graph collections

TL;DR

This paper introduces a new equivalence relation based on nodal statistics for analyzing graph collections, enabling better understanding of node roles and graph heterogeneity, with applications to brain connectivity data.

Contribution

It defines a novel nodal statistics-based equivalence relation and introduces global measures and a comparison method for graph families, advancing node role explainability in complex networks.

Findings

The method reveals differences in brain connectivity at global and nodal levels.

It effectively distinguishes between healthy controls and comatose patients.

High correspondence scores indicate homogeneity in healthy brain networks.

Abstract

Node role explainability in complex networks is very difficult, yet is crucial in different application domains such as social science, neurosciences or computer science. Many efforts have been made on the quantification of hubs revealing particular nodes in a network using a given structural property. Yet, in several applications, when multiple instances of networks are available and several structural properties appear to be relevant, the identification of node roles remains largely unexplored. Inspired by the node automorphically equivalence relation, we define an equivalence relation on graph nodes associated with any collection of nodal statistics (i.e. any functions on the node-set). This allows us to define new graph global measures: the power coefficient, and the orthogonality score to evaluate the parsimony and heterogeneity of a given nodal statistics collection. In addition, we introduce a new method based on structural patterns to compare graphs that have the same vertices set. This method assigns a value to a node to determine its role distinctiveness in a graph family. Extensive numerical results of our method are conducted on both generative graph models and real data concerning human brain functional connectivity. The differences in nodal statistics are shown to be dependent on the underlying graph structure. Comparisons between generative models and real networks combining two different nodal statistics reveal the complexity of human brain functional connectivity with differences at both global and nodal levels. Using a group of 200 healthy controls connectivity networks, our method computes high correspondence scores among the whole population, to detect homotopy, and finally quantify differences between comatose patients and healthy controls.