论文标题
多层网络中的社会传染和关联扩散
Social Contagion and Associative Diffusion in Multilayer Network
论文作者
论文摘要
文化变化如何出现的问题引起了人们对社会学探究的兴趣。社会学家主要通过社会传染的角度研究这种变化,这主要将文化变化归因于潜在的结构隔离,使其表现到现有的隔离结构。另一方面,争论文化并不像病毒那样传播,而是提出了一种称为联想扩散的替代性,其中文化传播不是出于实践而不是实践之间的关联而发生的。然后,关联扩散模型成功地解释了文化变化,而不将其归因于隔离的社会结构。传染模型和关联扩散模型需要不同类型的关系和相互作用,以使文化传播成为可能。实际上,存在两种类型的关系。鉴于这一问题,我们提出了将两个模型与多层网络框架相结合的。在一层中,代理商随便观察他人的行为,更新了他们对实践之间关联的信念。在另一层上,代理人对实践的偏爱直接受到封闭的其他人的影响。同时,偏好和关联之间的约束满意度用于将两者的更新联系起来,从而使每个人都成为偏好和关联方面的连贯实体。使用这种方法,我们通过多层网络纠缠着社会传染和关联扩散的影响。对于基线,我们探讨了三个通用网络模型的模型动力学:完全连接,小世界和无标度。结果表明,传染模型的两个极端与关联扩散模型之间的非平凡动力学,证明我们的说法是有必要同时考虑这两个模型。
The question that how cultural variation emerges has drawn lots of interest in sociological inquiry. Sociologists predominantly study such variation through the lens of social contagion, which mostly attributes cultural variation to the underlying structural segregation, making it epiphenomenal to the pre-existing segregated structure. On the other hand, arguing culture doesn't spread like a virus, an alternative called associative diffusion was proposed, in which cultural transmission occurs not at the preference of practices, but at the association between practices. The associative diffusion model then successfully explains cultural variation without attributing it to a segregated social structure. The contagion model and associative diffusion model require different types of relationships and interactions to make cultural transmission possible. In reality, both types of relationships exist. In light of this concern, we proposed combining the two models with the multilayer network framework. On one layer, agents casually observed the behaviors of others, updating their belief about the association between practices; on another layer, agents' preference of practices are directly influenced by closed others. In the meantime, the constraint satisfaction between preference and association is used to link the update of both, thereby making each individual a coherent entity in terms of preference and association. Using this approach, we entangle the effect of social contagion and associative diffusion through multilayer networks. For the baseline, we explore the model dynamics on three common network models: fully connected, small-world, and scale-free. The results show nontrivial dynamics between the two extremes of the contagion model and the associative diffusion model, justifying our claim that it is necessary to consider the two models at the same time.