Comparative analysis of graph randomization: Tools,methods, pitfalls, and best practices

TL;DR

This paper surveys graph randomization techniques, comparing tools and methods, highlighting pitfalls, and providing best practices to ensure accurate network analysis and interpretation.

Contribution

It offers a comprehensive comparison of software tools for graph randomization, illustrating how method choice impacts analysis outcomes and providing guidelines for best practices.

Findings

Different randomization methods can lead to divergent conclusions.

Tool functionalities and limitations vary significantly.

Careful method selection is crucial based on network characteristics.

Abstract

Graph randomization techniques play a crucial role in network analysis, allowing researchers to assess the statistical significance of observed network properties and distinguish meaningful patterns from random fluctuations. In this survey we provide an overview of the graph randomization methods available in the most popular software tools for network analysis. We propose a comparative analysis of popular software tools to highlight their functionalities and limitations. Through case studies involving diverse graph types, we demonstrate how different randomization methods can lead to divergent conclusions, emphasizing the importance of careful method selection based on the characteristics of the observed network and the research question at hand. This survey proposes some guidelines for researchers and practitioners seeking to understand and utilize graph randomization techniques effectively in their network analysis projects.