Emergence of clustering: Role of inhibition

TL;DR

This paper investigates how inhibition influences the emergence of clustering in complex networks, demonstrating through genetic algorithms that inhibition is essential for evolving clique structures and explaining observed triad distributions.

Contribution

It shows that inhibition is crucial for the evolution of clustering and clique structures in networks, providing a genetic algorithm-based demonstration and insights into real network patterns.

Findings

Inhibition is necessary for the evolution of clique structures.

Networks evolved with inhibition show triad distributions correlated with degree.

The study offers insights into the origin of clustering in biological and artificial systems.

Abstract

Though biological and artificial complex systems having inhibitory connections exhibit high degree of clustering in their interaction pattern, the evolutionary origin of clustering in such systems remains a challenging problem. Using genetic algorithm we demonstrate that inhibition is required in the evolution of clique structure from primary random architecture, in which the fitness function is assigned based on the largest eigenvalue. Further, the distribution of triads over nodes of the network evolved from mixed connections exhibits a negative correlation with its degree providing insight into origin of this trend observed in real networks.