Average Path Length in Complex Networks: Patterns and Predictions

TL;DR

This paper introduces a new relationship linking average shortest path, nodes, and edges in complex networks, improving prediction accuracy across various network scales by utilizing link density.

Contribution

It presents a simple, accurate formula that enhances the prediction of shortest paths in complex networks beyond traditional random graph models.

Findings

The new relationship significantly improves path length predictions.

The model applies effectively to networks of various sizes.

It outperforms traditional random graph models in fitting real networks.

Abstract

A simple and accurate relationship is demonstrated that links the average shortest path, nodes, and edges in a complex network. This relationship takes advantage of the concept of link density and shows a large improvement in fitting networks of all scales over the typical random graph model. The relationships herein can allow researchers to better predict the shortest path of networks of almost any size.