Phase transition in the majority-vote model on time-varying networks
Phase transition in the majority-vote model on time-varying networks
复制标题
时变网络上多数投票模型中的相变
DOI：
10.1103/physreve.105.014310
复制
发表时间：
2022-01-19
影响因子：
2.4
通讯作者：
Han，Yuexing
中科院分区：
文献类型：
--
作者：
Wang，Bing;Ding，Xu;Han，Yuexing
社会互动可能会影响个人观点的更新。多数投票（MV）模型等现有模型在不同的静态网络中得到了广泛的研究。然而，在现实中，社会网络随着时间的推移而变化，个人之间的互动是动态的。在这项工作中，我们研究了MV模型在时间网络上的行为，以分析时间性对意见动态的影响。在社交网络中，人们既可以主动地发送联系，也可以被动地接受联系，这对个体的观点产生了不同的影响。为了比较不同互动模式对意见动态的影响，我们将其简化为两个过程，即单定向（SD）过程和无定向（UD）过程。前者只允许每个个体通过跟随主动互动的大多数邻居来采纳意见，而后者允许每个个体通过跟随主动互动和被动互动的大多数邻居来改变意见。通过借鉴活动驱动的具有吸引力的时变网络（ADA模型），将两个意见更新过程即SD和UD过程与网络演化相关联。利用平均场法推导出了每个过程的临界噪声阈值，并通过数值模拟进行了验证。与SD过程相比，在相同的临界噪声下，UD过程具有更大的一致性水平。最后，我们还在实际网络中验证了主要结果。
Social interactions may affect the update of individuals' opinions. The existing models such as the majority-vote (MV) model have been extensively studied in different static networks. However, in reality, social networks change over time and individuals interact dynamically. In this work, we study the behavior of the MV model on temporal networks to analyze the effects of temporality on opinion dynamics. In social networks, people are able to both actively send connections and passively receive connections, which leads to different effects on individuals' opinions. In order to compare the impact of different patterns of interactions on opinion dynamics, we simplify them into two processes, that is, the single directed (SD) process and the undirected (UD) process. The former only allows each individual to adopt an opinion by following the majority of actively interactive neighbors, while the latter allows each individual to flip opinion by following the majority of both actively interactive and passively interactive neighbors. By borrowing the activity-driven time-varying network with attractiveness (ADA model), the two opinion update processes, i.e., the SD and the UD processes, are related with the network evolution. With the mean-field approach, we derive the critical noise threshold for each process, which is also verified by numerical simulations. Compared with the SD process, the UD process reaches a larger consensus level below the same critical noise. Finally, we also verify the main results in real networks.