标题： 基于贝叶斯社区检测的可靠数据聚类 显示英文标题

标题： Reliable data clustering with Bayesian community detection

摘要： 从神经科学和基因组学到系统生物学和生态学，研究人员依赖于聚类相似性数据来揭示模块结构。 然而广泛使用的聚类方法，如层次聚类、k均值和WGCNA，缺乏合理的模型选择，使其容易受到噪声的影响。 一种常见的解决方法是对相关矩阵表示进行稀疏化以在聚类前去除噪声，但这个额外步骤引入了任意的阈值，可能会扭曲结构并导致不可靠的结果。 为了检测可靠的聚类，我们利用网络科学的最新进展，将稀疏化和聚类与合理的模型选择结合起来。 我们测试了两种贝叶斯社区检测方法，即度校正随机块模型和正则化地图方程，它们都基于最小描述长度原则进行模型选择。 在合成数据中，它们优于传统方法，在高噪声条件下和样本较少的情况下都能检测到植入的聚类。 与WGCNA在基因共表达数据上的结果相比，正则化地图方程能识别出更多稳健且功能一致的基因模块。 我们的结果确立了贝叶斯社区检测作为一种合理且抗噪声的框架，用于在高维数据中揭示跨领域的模块结构。 显示英文摘要

摘要： From neuroscience and genomics to systems biology and ecology, researchers rely on clustering similarity data to uncover modular structure. Yet widely used clustering methods, such as hierarchical clustering, k-means, and WGCNA, lack principled model selection, leaving them susceptible to noise. A common workaround sparsifies a correlation matrix representation to remove noise before clustering, but this extra step introduces arbitrary thresholds that can distort the structure and lead to unreliable results. To detect reliable clusters, we capitalize on recent advances in network science to unite sparsification and clustering with principled model selection. We test two Bayesian community detection methods, the Degree-Corrected Stochastic Block Model and the Regularized Map Equation, both grounded in the Minimum Description Length principle for model selection. In synthetic data, they outperform traditional approaches, detecting planted clusters under high-noise conditions and with fewer samples. Compared to WGCNA on gene co-expression data, the Regularized Map Equation identifies more robust and functionally coherent gene modules. Our results establish Bayesian community detection as a principled and noise-resistant framework for uncovering modular structure in high-dimensional data across fields.