Emergence of (bi)multi-partiteness in networks having inhibitory and excitatory couplings

TL;DR

This paper investigates how networks with inhibitory and excitatory couplings naturally evolve into (bi)multi-partite structures, demonstrating stability and robustness through a genetic algorithm approach.

Contribution

It introduces a genetic algorithm method to show the emergence and stability of (bi)multi-partite network structures with inhibitory and excitatory interactions.

Findings

Networks evolve to (bi)multi-partite structures under stability constraints.

Evolved patterns are robust to initial conditions and interaction fluctuations.

Minimizing the largest eigenvalue promotes stable network configurations.

Abstract

(Bi)multi-partite interaction patterns are commonly observed in real world systems which have inhibitory and excitatory couplings. We hypothesize these structural interaction pattern to be stable and naturally arising in the course of evolution. We demonstrate that a random structure evolves to the (bi)multi-partite structure by imposing stability criterion through minimization of the largest eigenvalue in the genetic algorithm devised on the interacting units having inhibitory and excitatory couplings. The evolved interaction patterns are robust against changes in the initial network architecture as well as fluctuations in the interaction weights.