Data Science Laboratory, Ryerson University, Toronto, Ontario, Canada.
School of Government and Public Policy, University of Arizona, Tucson, Arizona, United States of America.
PLoS One. 2016 Oct 4;11(10):e0160307. doi: 10.1371/journal.pone.0160307. eCollection 2016.
This paper advances theories of social learning through an empirical examination of how social networks change over time. Social networks are important for learning because they constrain individuals' access to information about the behaviors and cognitions of other people. Using data on a large social network of mobile device users over a one-month time period, we test three hypotheses: 1) attraction homophily causes individuals to form ties on the basis of attribute similarity, 2) aversion homophily causes individuals to delete existing ties on the basis of attribute dissimilarity, and 3) social influence causes individuals to adopt the attributes of others they share direct ties with. Statistical models offer varied degrees of support for all three hypotheses and show that these mechanisms are more complex than assumed in prior work. Although homophily is normally thought of as a process of attraction, people also avoid relationships with others who are different. These mechanisms have distinct effects on network structure. While social influence does help explain behavior, people tend to follow global trends more than they follow their friends.
本文通过实证检验社会网络随时间的变化，提出了社会学习理论。社会网络对于学习很重要，因为它们限制了个人获取有关他人行为和认知信息的机会。我们利用一个月时间内关于大型移动设备用户社交网络的数据，检验了三个假设：1）吸引同质性导致个体基于属性相似性形成联系，2）厌恶同质性导致个体基于属性差异删除现有联系，3）社会影响导致个体采用与其具有直接联系的其他人的属性。统计模型对所有三个假设都提供了不同程度的支持，并表明这些机制比之前的工作所假设的更为复杂。尽管同质性通常被认为是一种吸引的过程，但人们也会避免与不同的人建立关系。这些机制对网络结构有不同的影响。虽然社会影响确实有助于解释行为，但人们往往更倾向于跟随全球趋势，而不是跟随他们的朋友。
