Correlations among Game of Thieves and other centrality measures in complex networks

TL;DR

This paper explores the correlation between the Game of Thieves algorithm and traditional centrality measures in social networks, demonstrating that GoT can serve as an efficient alternative for large-scale network analysis.

Contribution

The study introduces the use of the Game of Thieves algorithm to efficiently compute centrality, revealing strong correlations with classical measures in complex networks.

Findings

Strong correlation between GoT and traditional centrality measures.

GoT computes importance in polylogarithmic time, outperforming classical methods.

GoT is suitable for large-scale complex network analysis.

Abstract

Social Network Analysis (SNA) is used to study the exchange of resources among individuals, groups, or organizations. The role of individuals or connections in a network is described by a set of centrality metrics which represent one of the most important results of SNA. Degree, closeness, betweenness and clustering coefficient are the most used centrality measures. Their use is, however, severely hampered by their computation cost. This issue can be overcome by an algorithm called Game of Thieves (GoT). Thanks to this new algorithm, we can compute the importance of all elements in a network (i.e. vertices and edges), compared to the total number of vertices. This calculation is done not in a quadratic time, as when we use the classical methods, but in polylogarithmic time. Starting from this we present our results on the correlation existing between GoT and the most widely used centrality measures. From our experiments emerge that a strong correlation exists, which makes GoT eligible as a centrality measure for large scale complex networks.