Evaluate dynamic network with evolutionary game method

The application of evolutionary games on complex networks has made a great difference. In this paper, an optimized evolutionary game method based on public goods games (PGG) is put forward to describe and evaluate time-varying mixed membership networks. Considering the heterogeneous topology, a new preferential rule is proposed to quantify the process of choosing and updating the payoff of individuals in the public goods games. Each individual is allocated with a weight to restrict the influence. The optimal parameter is obtained by minimizing the entropy of nodes topological potential, an efficient way to depict the effect among individuals, which is inspired by Gaussian potential of data field. It demonstrates that an appropriate constraint on individuals does make it more like to approach to the reality, and when it comes to specific conditions, the proposed model achieves well performance.

[1]  Alexandre d'Aspremont,et al.  Model Selection Through Sparse Max Likelihood Estimation Model Selection Through Sparse Maximum Likelihood Estimation for Multivariate Gaussian or Binary Data , 2022 .

[2]  Zhou Tao.  Dissipation Cascading Dynamics on Regular Networks , 2005 .

[3]  Zhou Tao.  Researches of Human Dynamics , 2010 .

[4]  Y. Lai,et al.  Diversity-optimized cooperation on complex networks. , 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.

[5]  Wang Bing-Hong.  Human Dynamic Model Based on Adaptive Adjustment , 2007 .

[6]  Jian-Min Wang,et al.  Community Discovery Method in Networks Based on Topological Potential: Community Discovery Method in Networks Based on Topological Potential , 2009 .

[7]  Víctor M Eguíluz,et al.  Cooperation, social networks, and the emergence of leadership in a prisoner's dilemma with adaptive local interactions. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[8]  F. C. Santos,et al.  Social diversity promotes the emergence of cooperation in public goods games , 2008, Nature.

[9]  C. Hauert,et al.  Via Freedom to Coercion: The Emergence of Costly Punishment , 2007, Science.

[10]  D. Helbing,et al.  The outbreak of cooperation among success-driven individuals under noisy conditions , 2009, Proceedings of the National Academy of Sciences.

[11]  Wen-Xu Wang,et al.  Effect of clustering coefficient on cooperation in scale-free public goods game , 2010, Proceedings of 2010 IEEE International Symposium on Circuits and Systems.

[12]  Attila Szolnoki,et al.  Evolutionary Establishment of Moral and Double Moral Standards through Spatial Interactions , 2010, PLoS Comput. Biol..

[13]  Tao Zhou,et al.  Towards the understanding of human dynamics , 2008 .

[14]  G. Szabó,et al.  Cooperation for volunteering and partially random partnerships. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[15]  Gan Wen.  Community Discovery Method in Networks Based on Topological Potential , 2009 .

[16]  Zhou Tao.  Human Activity Pattern on On-line Movie Watching , 2008 .

[17]  Herbert W. Hethcote,et al.  The Mathematics of Infectious Diseases , 2000, SIAM Rev..

[18]  R Pastor-Satorras,et al.  Dynamical and correlation properties of the internet. , 2001, Physical review letters.