McMillan Cassie
Northeastern University.
Soc Networks. 2022 Jan;68:139-147. doi: 10.1016/j.socnet.2021.06.003. Epub 2021 Jun 17.
Homophily, or the tendency for social contact to occur among those who are similar, plays a crucial role in structuring our social networks. Most previous work considers whether homophily shapes the patterns of all social ties, regardless of their frequency of interaction or level of intimacy. As complex network data become increasingly available, however, researchers need to evaluate whether homophily operates differently for ties defined by strong versus weak measures of strength. Here, I take this approach by first defining two variants of homophily: (1) , or the tendency for ties with high measures of strength to cluster together similar peers, and (2) , or the tendency for ties with low edge weights to connect same-attribute actors. Then, I apply valued ERGMs to demonstrate the utility of differentiating between the two variants across simulated and observed networks. In most networks, I find that there are observable differences in the magnitude of strong versus weak tie homophily. Additionally, when there are low levels of clustering on the attribute of interest, distinguishing between strong and weak tie homophily can reveal that these processes operate in opposite directions. Since strong and weak ties carry substantively different implications, I argue that differentiating between the two homophily variants has the potential to uncover novel insights on a variety of social phenomena.
同质性，即社会交往倾向于在相似的人之间发生，在构建我们的社会网络中起着至关重要的作用。以前的大多数研究都在考虑同质性是否塑造了所有社会关系的模式，而不论其互动频率或亲密程度如何。然而，随着复杂网络数据越来越容易获取，研究人员需要评估同质性对于由强弱不同的强度度量所定义的关系是否有不同的作用方式。在这里，我采用的方法是首先定义同质性的两种变体：（1）强关系同质性，即高强度关系倾向于将相似的同龄人聚集在一起的趋势；（2）弱关系同质性，即低边权重关系倾向于连接同属性参与者的趋势。然后，我应用有价值的指数随机图模型（ERGM）来证明在模拟网络和观察到的网络中区分这两种变体的效用。在大多数网络中，我发现强关系同质性和弱关系同质性在程度上存在可观察到的差异。此外，当感兴趣的属性上的聚类水平较低时，区分强关系同质性和弱关系同质性可以揭示这些过程以相反的方向运作。由于强关系和弱关系具有实质上不同的含义，我认为区分这两种同质性变体有可能揭示关于各种社会现象的新见解。
