基于演化博弈的社交网络模型演化研究

摘要

The development of research on social network makes a great contribution to the study of network evolution though much of the work focuses on a macroscopic evolutionary mechanism. In this paper, based on public goods games, an optimized evolution-ary dynamic multi-community network model generated by the co-evolution process of evolutionary games and network topology is put forward (dMCPGG). Edges are revised according to the difference between expected payoff and effective payoff through time. Considering the heterogeneous topology, a new preferential rule based on the topological potential is introduced to quantify the nodes’ importance when choosing and updating the payoff of individuals in the public goods games. Finally, the results of simulations demon-strate that the dMCPGG model can reproduce the random world and scale-free world features, such as the nodes’ degree, clustering coefficient and average path length. Finally, we apply our model to United State Congress voting data and verify its rationality.%社会网络研究的兴起，为网络演化规律研究提供了有效工具，但大多数研究集中从宏观机制评估网络演化的动态过程。本文基于公共品博弈，通过演化博弈与网络拓扑共演化方式，从微观角度提出了多社区动态网络演化模型(dMCPGG)。即以节点间演化博弈为动力，修改节点间边的关系，驱动网络拓扑演化。考虑到网络异质性，采用基于拓扑势的偏好规则更准确全面的描述节点影响力。通过数值模拟和仿真实验，验证了本模型的合理性，不仅重现了无标度网络及随机网络的节点度、聚类系数及平均路径长度的结构特性，还准确捕捉到真实社交网络的演化过程。