Self-organized Model for Modular Complex Networks : Division and Independence

TL;DR

This paper presents a self-organized network model that generates modular structures through division and independence, successfully replicating hierarchical clustering behaviors observed in real-world networks.

Contribution

It introduces a novel modification of the Barabasi-Albert model incorporating division and independence principles for modular network formation.

Findings

Model reproduces hierarchical clustering coefficient C(k) accurately.

Successfully simulates modular structures akin to real-world networks.

Demonstrates self-organization in network modularity.

Abstract

We introduce a minimal network model which generates a modular structure in a self-organized way. To this end, we modify the Barabasi-Albert model into the one evolving under the principle of division and independence as well as growth and preferential attachment (PA). A newly added vertex chooses one of the modules composed of existing vertices, and attaches edges to vertices belonging to that module following the PA rule. When the module size reaches a proper size, the module is divided into two, and a new module is created. The karate club network studied by Zachary is a prototypical example. We find that the model can reproduce successfully the behavior of the hierarchical clustering coefficient of a vertex with degree k, C(k), in good agreement with empirical measurements of real world networks.