Institute of Environmental Science and Technology, Universitat Autónoma de Barcelona, Spain.
Graduate School of Economics and Management, Ural Federal University, Yekaterinburg, Russian Federation.
Phys Rev E. 2019 Aug;100(2-1):022305. doi: 10.1103/PhysRevE.100.022305.
The speed and extent of diffusion of behaviors in social networks depends on network structure and individual preferences. The contribution of the present study is twofold. First, we introduce weighted interactions between potential adopters that depend on the similarity in their preferences and moderate the strength of social reinforcement. The reason for the extension is the existence of a confirmation bias in the way agents treat information by prioritizing evidence conforming to their opinion. As a result, individuals become less likely to be influenced by peers with relatively different preferences, reducing the overall diffusion rate under clustered networks. Second, we enrich our analysis by also considering a scale free network topology with a high degree asymmetry, motivated by its pervasiveness in online social networks. This network performs consistently well in terms of diffusion for different parameter combinations and clearly outperforms clustered networks under weighted interactions. Our results show that more realistic assumptions regarding agents' interactions shift the focus from clustering to degree distribution in the study of network structures allowing for fast and widespread behavior adoption.
行为在社交网络中的扩散速度和程度取决于网络结构和个体偏好。本研究的贡献有两点。首先，我们在潜在采纳者之间引入了依赖于偏好相似性的加权交互，从而调节了社会强化的强度。扩展的原因是由于代理人在处理信息时存在确认偏差，即优先考虑符合其观点的证据。结果是，个体不太可能受到具有相对不同偏好的同行的影响，从而降低了聚类网络下的整体扩散速度。其次，我们通过还考虑具有高度不对称性的无标度网络拓扑结构来丰富我们的分析，这是因为它在在线社交网络中普遍存在。这种网络在不同参数组合下的扩散性能表现一致，并且在加权交互下明显优于聚类网络。我们的结果表明，关于代理交互的更现实假设将研究的重点从聚类转移到网络结构的度分布，从而允许快速和广泛的行为采用。
