Nodes having a major influence to break cooperation define a novel centrality measure: game centrality

Abstract

Cooperation played a significant role in the self-organization and evolution of living organisms. Both network topology and the initial position of cooperators heavily affect the cooperation of social dilemma games. We developed a novel simulation program package, called 'NetworGame', which is able to simulate any type of social dilemma games on any model, or real world networks with any assignment of initial cooperation or defection strategies to network nodes. The ability of initially defecting single nodes to break overall cooperation was called as 'game centrality'. The efficiency of this measure was verified on well-known social networks, and was extended to 'protein games', i.e. the simulation of cooperation between proteins, or their amino acids. Hubs and in particular, party hubs of yeast protein-protein interaction networks had a large influence to convert the cooperation of other nodes to defection. Simulations on methionyl-tRNA synthetase protein structure network indicated an increased influence of nodes belonging to intra-protein signaling pathways on breaking cooperation. The efficiency of single, initially defecting nodes to convert the cooperation of other nodes to defection in social dilemma games may be an important measure to predict the importance of nodes in the integration and regulation of complex systems. Game centrality may help to design more efficient interventions to cellular networks (in forms of drugs), to ecosystems and social networks. The NetworGame algorithm is downloadable from here: www.NetworGame.linkgroup.hu

18 pages, 2 figures, 3 Tables + a supplement containing 8 pages, 1 figure, 2 Tables and the pseudo-code of the algorithm, the NetworGame algorithm is downloadable from here: http://www.NetworGame.linkgroup.hu

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• Optimal interdependence between networks for the evolution of cooperation
• Degree mixing in multilayer networks impedes the evolution of cooperation
• Self-organization towards optimally interdependent networks by means of coevolution
• Spreading of cooperative behaviour across interdependent groups
• Optimal distribution of incentives for public cooperation in heterogeneous interaction environments