Salehzadeh-Yazdi Ali, Hütt Marc-Thorsten
School of Science, Constructor University, Bremen, Germany.
NPJ Syst Biol Appl. 2025 May 15;11(1):47. doi: 10.1038/s41540-025-00526-w.
Centrality measures are crucial for network analysis, offering insights into node importance within complex networks. However, their accuracy is often affected by observational errors and incomplete data. This study investigates how sampling biases systematically impact centrality measures. We simulate six types of biased down-sampling, transitioning networks from dense to sparse states, using the initial network as the 'ground truth.' Changes in centrality values reveal the robustness of these measures under various sampling scenarios across synthetic and biological networks. Our results show that in synthetic networks, some sampling methods consistently exhibit higher robustness, particularly in scale-free networks. For biological networks, protein interaction networks are the most robust, followed by metabolite, gene regulatory, and reaction networks. Local centrality measures generally show greater robustness, while global measures are more heterogeneous and less reliable. This study highlights the limitations of centrality measures under sampling biases and informs the development of more robust methodologies.
中心性度量对于网络分析至关重要，它能深入了解复杂网络中节点的重要性。然而，其准确性常常受到观测误差和不完整数据的影响。本研究调查了抽样偏差如何系统性地影响中心性度量。我们模拟了六种类型的有偏下采样，将网络从密集状态转变为稀疏状态，以初始网络作为“真实情况”。中心性值的变化揭示了这些度量在合成网络和生物网络的各种采样场景下的稳健性。我们的结果表明，在合成网络中，一些采样方法始终表现出更高的稳健性，尤其是在无标度网络中。对于生物网络，蛋白质相互作用网络最为稳健，其次是代谢物、基因调控和反应网络。局部中心性度量通常表现出更高的稳健性，而全局度量则更加参差不齐且可靠性较低。本研究突出了抽样偏差下中心性度量的局限性，并为更稳健方法的开发提供了参考。
