Department of Biostatistics, Brown University, Providence, Rhode Island, USA.
Department of Pharmacy Practice, University of Rhode Island, Providence, Rhode Island, USA.
Biometrics. 2023 Dec;79(4):3715-3727. doi: 10.1111/biom.13841. Epub 2023 Feb 28.
Researchers across a wide array of disciplines are interested in finding the most influential subjects in a network. In a network setting, intervention effects and health outcomes can spill over from one node to another through network ties, and influential subjects are expected to have a greater impact than others. For this reason, network research in public health has attempted to maximize health and behavioral changes by intervening on a subset of influential subjects. Although influence is often defined only implicitly in most of the literature, the operative notion of influence is inherently causal in many cases: influential subjects are those we should intervene on to achieve the greatest overall effect across the entire network. In this work, we define a causal notion of influence using potential outcomes. We review existing influence measures, such as node centrality, that largely rely on the particular features of the network structure and/or on certain diffusion models that predict the pattern of information or diseases spreads through network ties. We provide simulation studies to demonstrate when popular centrality measures can agree with our causal measure of influence. As an illustrative example, we apply several popular centrality measures to the HIV risk network in the Transmission Reduction Intervention Project and demonstrate the assumptions under which each centrality can represent the causal influence of each participant in the study.
研究人员在广泛的学科领域都对发现网络中最具影响力的主体感兴趣。在网络环境中，干预效果和健康结果可以通过网络联系从一个节点传播到另一个节点，而有影响力的主体预计会产生比其他主体更大的影响。出于这个原因，公共卫生领域的网络研究试图通过对有影响力的主体的子集进行干预，来最大程度地实现健康和行为改变。尽管在大多数文献中，影响通常只是隐含定义的，但在许多情况下，影响的操作性概念本质上是因果关系的：有影响力的主体是我们应该干预的对象，以在整个网络中实现最大的整体效果。在这项工作中，我们使用潜在结果来定义因果影响的概念。我们回顾了现有的影响衡量指标，如节点中心性，这些指标主要依赖于网络结构的特定特征和/或某些扩散模型，这些模型预测信息或疾病通过网络联系传播的模式。我们提供模拟研究结果，以展示在哪些情况下流行的中心性衡量指标可以与我们的因果影响衡量指标一致。作为一个说明性的例子，我们将几种流行的中心性衡量指标应用于 HIV 风险网络，在 Transmission Reduction Intervention Project 中，并展示了每种中心性可以代表每个参与者的因果影响的假设。
