A simple and flexible algorithm to generate real-world networks

TL;DR

This paper presents a simple, flexible algorithm for generating undirected networks that mimic real-world properties like scale-freeness, small-world effects, and high clustering, without requiring global network information.

Contribution

The authors introduce a novel, easy-to-implement algorithm that can produce realistic network structures with tunable properties based on local random walk processes.

Findings

The algorithm successfully generates networks with scale-free degree distributions.

It can produce networks with adjustable average distances and clustering coefficients.

The implementation is publicly available for use and further research.

Abstract

This study introduces an algorithm that generates undirected graphs with three main characteristics of real-world networks: scale-freeness, short distances between nodes (small-world phenomenon), and large clustering coefficients. The main idea is to perform random walks across the network and, at each iteration, add special edges with a decreasing probability to link more distant nodes, following a specific probability distribution. A key advantage of our algorithm is its simplicity and flexibility in creating networks with different characteristics without using global information about network topology. We show how the parameters can be adjusted to generate networks with specific average distances and clustering coefficients, maintaining a long-tailed degree distribution. The implementation of our algorithm is publicly available on a GitHub repository.