"Winner takes it all": strongest node rule for evolution of scale free networks

TL;DR

This paper introduces a new model for scale-free network evolution combining stochastic sampling and deterministic attachment to high-degree nodes, resulting in degree distributions with power-law behavior and exponential cutoff.

Contribution

It presents a novel network growth mechanism using information filtering and the strongest node rule, providing both simulation and theoretical analysis.

Findings

Degree distributions follow a power-law with exponential cutoff.

The model captures essential features of information filtering in network growth.

Results demonstrate the impact of the strongest node rule on network topology.

Abstract

We study a novel model for evolution of complex networks. We introduce information filtering for reduction of the number of available nodes to a randomly chosen sample, as stochastic component of evolution. New nodes are attached to the nodes that have maximal degree in the sample, which is a deterministic component of network evolution process. This fact is a novel for evolution of scale free networks and depicts a possible new route for modeling network growth. We present both simulational and theoretical results for network evolution. The obtained degree distributions exhibit an obvious power-law behavior in the middle with the exponential cut off in the end. This highlights the essential characteristics of information filtering in the network growth mechanisms.