Evolving Models for Meso-Scale Structures

TL;DR

This paper introduces evolving models for complex networks that capture meso-scale structures like core-periphery and community organization, validated through decomposition methods and simulation results.

Contribution

It presents novel models for the evolution of unweighted and weighted scale-free networks incorporating local and global meso-scale structures.

Findings

Generated networks follow power law degree and weight distributions.

Models replicate clustering coefficient and degree correlation properties.

Simulation confirms the presence of core-periphery and community structures.

Abstract

Real world complex networks are scale free and possess meso-scale properties like core-periphery and community structure. We study evolution of the core over time in real world networks. This paper proposes evolving models for both unweighted and weighted scale free networks having local and global core-periphery as well as community structure. Network evolves using topological growth, self growth, and weight distribution function. To validate the correctness of proposed models, we use K-shell and S-shell decomposition methods. Simulation results show that the generated unweighted networks follow power law degree distribution with droop head and heavy tail. Similarly, generated weighted networks follow degree, strength, and edge-weight power law distributions. We further study other properties of complex networks, such as clustering coefficient, nearest neighbor degree, and strength degree correlation.