Abstract
AbstractAs the most popular sport around the globe, the game of football has recently intrigued much research interest to explore and distill useful and appealing information from the sport. Network science and graph-centric methods have been previously applied to study the importance of football players and teams. In this paper, for the first time we study the macroscopic evolution of the football society from a complex network point of view. Football game records within a time window of over a century were collected and expressed in a graph format, where participant teams are represented by graph nodes and the games between them are the graph edges. We carry out community detection and temporal analysis to reveal the dynamic features and the community structures embedded within the football network, offering the evidence of a continuously expanding football society. Spatio-temporal analysis is also implemented to unveil the temporal states that represent distinct development stages in the football history. Our analysis suggests that the evolution of the game receives considerable impact not only from major sport events, but also from multiple social and political incidents. The game of football and its evolution reflect significant historical transitions and turning points, and can provide a novel perspective for the study of the worldwide globalization process.
Funder
National Science Foundation
Publisher
Springer Science and Business Media LLC
Subject
Computational Mathematics,Computer Networks and Communications,Multidisciplinary
Reference43 articles.
1. Agrawal R, Mannila H, Srikant R, Toivonen H, Verkamo AI et al (1996) Fast discovery of association rules. Adv Knowl Discov Data Min 12(1):307–328
2. Bastian M, Heymann S, Jacomy M (2009) Gephi: an open source software for exploring and manipulating networks. Proc Int AAAI Conf Web Soc Media 3:361–362
3. Black PE (2008) Sparse Graph. Dictionary of algorithms and data structures, National Institute of Standards and Technology (NIST)
4. Blondel VD, Guillaume J-L, Lambiotte R, Lefebvre E (2008) Fast unfolding of communities in large networks. J Stat Mech Theory Exp 2008(10):10008
5. Bunke H (2000) Graph matching: theoretical foundations, algorithms, and applications. Proc Vis Interface 2000:82–88
Cited by
3 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献