论文标题
一种自适应网络模型，以模拟由社会身份识别驱动的共识形成
An Adaptive Networks Model to Simulate Consensus Formation Driven by Social Identity Recognition
论文作者
论文摘要
各个国家在社会系统中共识的模型一直是物理文献中最新研究的主题。我们研究了网络结构如何根据社会认同理论的框架与个体状态相结合。我们提出了一个自适应网络模型，以通过评估他们之间的同质性来实现国家共识或局部结构调整。具体而言，相似性阈值显着影响网络在不同初始条件下的演变，因此出现了明显的社区结构和极化。更重要的是，存在一个临界点的关键点，网络可能会演变成一个重要的社区结构和国家一致的群体。
Models of the consensus of the individual state in social systems have been the subject of recent researches in the physics literature. We investigate how network structures coevolve with the individual state under the framework of social identity theory. And we propose an adaptive network model to achieve state consensus or local structural adjustment of individuals by evaluating the homogeneity among them. Specifically, the similarity threshold significantly affects the evolution of the network with different initial conditions, and thus there emerges obvious community structure and polarization. More importantly, there exists a critical point of phase transition, at which the network may evolve into a significant community structure and state-consistent group.
