Centrality metrics are vital for network analysis, but selecting the most appropriate measures for specific applications remains challenging among the 400+ proposed indices. Existing approaches -- model-based, data-driven, and axiomatic -- have limitations. To address this, we introduce the culling method, leveraging expert preferences regarding centrality behavior on simple graphs. It involves forming a set of candidate measures, generating a list of as small graphs as possible needed to ``separate'' measures from each other, constructing a decision-tree survey, and identifying the measure consistent with expert responses. We apply this method to a diverse set of 40 centralities, including new kernel-based measures, and combine it with the axiomatic approach. Remarkably, only 13 small 1-trees suffice to separate all 40 measures, among which there are pairs of close ones. The culling method offers a low-cost solution in terms of labor and time, complements existing methods for measure selection, and reveals important peculiarities of centrality measures.

翻译：中心性度量对网络分析至关重要，但在400多种已提出的指标中，为特定应用选择最合适的度量仍具挑战性。现有方法——基于模型、数据驱动和公理化——均存在局限性。为解决这一问题，我们引入筛选方法，利用专家对简单图上中心性行为的偏好。该方法包括：形成候选度量集，生成尽可能小的图列表以“区分”各度量，构建决策树调查问卷，并识别与专家响应一致的度量。我们将该方法应用于40种多样化的中心性度量（包括新的基于核的度量），并与公理化方法相结合。值得注意的是，仅需13个小的1-树即可区分全部40种度量，其中包含若干相近的度量对。筛选方法在人工和时间成本上提供了低开销解决方案，补充了现有的度量选择方法，并揭示了中心性度量的重要特性。