Considerations of bias, fairness and representation are a prerequisite of responsible modern statistics. In statistical network analysis, observed networks are often incomplete or systematically biased, which can lead to systematic underrepresentation of protected groups, and affect any downstream ranking or decision based on the observed network. In this paper, we study a high school contact network constructed from self-reported contact diaries and introduce a formal measure of minority representation, defined as the proportion of minority nodes among the top-ranked individuals. We model systematic bias through group-dependent missing edge mechanisms and develop statistical methods to estimate and test for such bias. When bias is detected, we propose a re-ranking procedure based on an asymptotic approximation that improves group representation. Applying the framework to the high school contact network reveals systematic underreporting of cross-group contacts consistent with recall bias. These findings highlight the importance of modeling and correcting systematic bias in social networks with heterogeneous groups.

翻译：对偏差、公平性与表征的考量是现代负责任统计学的前提。在统计网络分析中，观测网络往往不完整或存在系统性偏差，这可能导致受保护群体的系统性表征不足，并影响基于观测网络的任何下游排名或决策。本文基于自我报告的接触日记构建高中接触网络，引入一种正式的少数群体表征度量，定义为排名靠前个体中少数群体节点的比例。我们通过群体依赖的边缺失机制对系统性偏差进行建模，并开发统计方法来估计和检验此类偏差。当检测到偏差时，我们提出一种基于渐近近似的重排名方法以改善群体表征。将该框架应用于高中接触网络，揭示了跨群体接触的系统性漏报现象，这与回忆偏差一致。这些发现凸显了在具有异质性群体的社交网络中建模并校正系统性偏差的重要性。