Features of Evolutionary Complex Networks in Complex Adaptive Systems
Xiangyun Gao, Haizhong An, Huajiao Li, Lijun Wang, Xiaoqi Sun, Feng An
Abstract
Xiangyun Gao, Haizhong An, Huajiao Li, Lijun Wang, Xiaoqi Sun, Feng An
Abstract
Adaptive behaviours of nodes can cause the evolution of networks. To investigate the characteristics of evolutionary complex networks that result from the interactions between agents in complex adaptive systems, we construct a complex network model of heat-bug interactions by drawing on statistical physics methods based on the heat-bug experiment. The networks of interactions between heat bugs show the following: that the degree distribution evolves from the Gamma distribution to the Gaussian distribution, that the average local density is high, and the average path length is short, and that the dynamics of clusters in the networks changes from creation to combination and then to separation, and the members of the clusters change continuously, but the number of clusters remains stable. Based on the micro perspective, these results provide a good alternative to explain emerging complex phenomena by elucidating the characteristics of evolutionary complex networks.
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Adaptive behaviours of nodes can cause the evolution of networks. To investigate the characteristics of evolutionary complex networks that result from the interactions between agents in complex adaptive systems, we construct a complex network model of heat-bug interactions by drawing on statistical physics methods based on the heat-bug experiment. The networks of interactions between heat bugs show the following: that the degree distribution evolves from the Gamma distribution to the Gaussian distribution, that the average local density is high, and the average path length is short, and that the dynamics of clusters in the networks changes from creation to combination and then to separation, and the members of the clusters change continuously, but the number of clusters remains stable. Based on the micro perspective, these results provide a good alternative to explain emerging complex phenomena by elucidating the characteristics of evolutionary complex networks.
Key concepts: Complex network, Complex adaptive system, Complex system, Degree distribution, Computer science, Construct (python library), Average path length, Gaussian