可解释的随机块影响模型：测量同性恋社区的社会影响力
Interpretable Stochastic Block Influence Model: measuring social influence among homophilous communities
摘要：
网络上的决策可以用同质性和社会影响来解释。虽然同质性驱动着具有相似特征的社区的形成，但社会影响既发生在社区内部，也发生在社区之间。社会影响可以通过角色理论来解释，即个体之间的影响取决于他们的角色和利益行为。为了操作这些社会科学理论，我们经验性地确定同性恋社区，并使用社区结构来捕捉“角色”，这影响了特定的决策过程。我们提出了一个生成模型命名为随机块影响模型，并共同分析网络的形成和行为的影响，在不同的社区内和不同的社区之间。为了评估性能和证明我们的方法的可解释性，我们研究了在印度的一个村庄的小额信贷的采用决定。我们发现，虽然个人倾向于形成社区内的联系，社区之间有很强的积极和消极的社会影响，支持弱关系理论。此外，我们发现具有共同特征的社区与积极影响有关。相反，缺乏重叠的社区与负面影响有关。我们的框架有利于量化的影响决策社区，因此是一个有用的工具，推动信息扩散，病毒式营销和技术采用。
英文摘要：
Decision-making on networks can be explained by both homophily and social influence. While homophily drives the formation of communities with similar characteristics, social influence occurs both within and between communities. Social influence can be reasoned through role theory, which indicates that the influences among individuals depend on their roles and the behavior of interest. To operationalize these social science theories, we empirically identify the homophilous communities and use the community structures to capture the "roles", which affect the particular decision-making processes. We propose a generative model named Stochastic Block Influence Model and jointly analyze both the network formation and the behavioral influence within and between different empirically-identified communities. To evaluate the performance and demonstrate the interpretability of our method, we study the adoption decisions of microfinance in an Indian village. We show that although individuals tend to form links within communities, there are strong positive and negative social influences between communities, supporting the weak tie theory. Moreover, we find that communities with shared characteristics are associated with positive influence. In contrast, the communities with a lack of overlap are associated with negative influence. Our framework facilitates the quantification of the influences underlying decision communities and is thus a useful tool for driving information diffusion, viral marketing, and technology adoptions.
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