Structure constrained by metadata in networks of chess players

TL;DR

This study constructs and analyzes networks of chess players from over-the-board and online games, revealing community structures based on skill levels and a rich-club phenomenon among top-rated players.

Contribution

It introduces a novel approach by integrating player metadata into network analysis, uncovering community and rich-club structures in chess player networks.

Findings

Players form communities based on expertise levels.

Rich-club structure exists among top-rated players.

Networks differ topologically but show similar community patterns.

Abstract

Chess is an emblematic sport that stands out because of its age, popularity and complexity. It has served to study human behavior from the perspective of a wide number of disciplines, from cognitive skills such as memory and learning, to aspects like innovation and decision making. Given that an extensive documentation of chess games played throughout history is available, it is possible to perform detailed and statistically significant studies about this sport. Here we use one of the most extensive chess databases in the world to construct two networks of chess players. One of the networks includes games that were played over-the-board and the other contains games played on the Internet. We study the main topological characteristics of the networks, such as degree distribution and correlations, transitivity and community structure. We complement the structural analysis by incorporating players' level of play as node metadata. Although both networks are topologically different, we show that in both cases players gather in communities according to their expertise and that an emergent rich-club structure, composed by the top-rated players, is also present.