Rank-dependent deactivation in network evolution

TL;DR

This paper introduces a rank-dependent deactivation model for network evolution, capturing key features of real-world networks such as power-law degree distribution, high clustering, and disassortative mixing.

Contribution

It proposes a novel deactivation mechanism based on finite memory, providing a simple model that reproduces several characteristic properties of complex networks.

Findings

Network exhibits power-law degree distribution

High clustering coefficient observed

Disassortative degree correlation present

Abstract

A rank-dependent deactivation mechanism is introduced to network evolution. The growth dynamics of the network is based on a finite memory of individuals, which is implemented by deactivating one site at each time step. The model shows striking features of a wide range of real-world networks: power-law degree distribution, high clustering coefficient, and disassortative degree correlation.