Adaptive Dynamics of Realistic Small-World Networks

TL;DR

This paper analyzes a dynamic algorithm that creates and adapts small-world networks in various realistic settings, demonstrating robustness and improved performance in diverse geographic and popularity distribution scenarios.

Contribution

It introduces an experimental analysis of a dynamic network formation algorithm that adapts to different real-world and synthetic data, showing its robustness and efficiency.

Findings

The algorithm adapts robustly to diverse geographic networks.

It improves performance when vertices follow non-uniform distributions.

The process suggests a natural mechanism for small-world emergence.

Abstract

Continuing in the steps of Jon Kleinberg's and others celebrated work on decentralized search in small-world networks, we conduct an experimental analysis of a dynamic algorithm that produces small-world networks. We find that the algorithm adapts robustly to a wide variety of situations in realistic geographic networks with synthetic test data and with real world data, even when vertices are uneven and non-homogeneously distributed. We investigate the same algorithm in the case where some vertices are more popular destinations for searches than others, for example obeying power-laws. We find that the algorithm adapts and adjusts the networks according to the distributions, leading to improved performance. The ability of the dynamic process to adapt and create small worlds in such diverse settings suggests a possible mechanism by which such networks appear in nature.