带协变量的噪声网络的稳健社区检测：应用于功能性脑网络
Robust Community Detection for Noisy Networks with Covariates: Application to Functional Brain Networks
- University of Connecticut（康涅狄格大学）
- University of Pennsylvania（宾夕法尼亚大学）
- University of Texas MD Anderson Cancer Center（德克萨斯大学安德森癌症中心）
- Yale University（耶鲁大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对带噪声和协变量的功能性脑网络，提出贝叶斯框架结合度校正随机块模型与噪声模型，用MCMC和WAIC实现稳健社区检测，在模拟和真实数据中优于现有方法。
AI中文摘要：
社区检测对于理解功能性脑网络中的模块化组织至关重要，然而神经影像衍生网络中的噪声以及辅助的节点级协变量构成了关键挑战。现有方法通常要么假设网络无噪声，要么忽略协变量信息。我们提出了一个贝叶斯框架，用于从多个含噪网络实现和辅助协变量中恢复共享的潜在社区结构。该模型结合了用于潜在网络的度校正随机块模型、将噪声观测与潜在边联系起来的块结构噪声模型，以及用于节点级属性的协变量聚类模型。这种设定使得感兴趣区域的解剖或功能属性能够在网络信号较弱或稀疏时提供信息。我们开发了一种高效的马尔可夫链蒙特卡洛算法用于后验采样，并使用广泛适用信息准则选择社区数量，从而避免预先指定该数量。模拟研究表明，在不同噪声水平、协变量信号强度和含噪网络数量下，相对于现有方法，社区恢复性能有所提升，且当网络噪声为中等到较高或仅有少量含噪网络可用时，提升幅度更大。应用于阿尔茨海默病神经影像倡议和人类连接组计划的功能性脑网络，识别出具有生物学可解释性的结构，并捕捉了与疾病相关的重组和个体水平变异。
英文摘要：
Community detection is fundamental to understanding the modular organization in functional brain networks, yet noise in neuroimaging-derived networks and auxiliary node-level covariates pose critical challenges. Existing methods typically either assume networks are noise-free or ignore covariate information. We propose a Bayesian framework for recovering a shared latent community structure from multiple noisy network realizations and auxiliary covariates. The model combines a degree-corrected stochastic block model for the latent network, a block-structured noise model linking noisy observations to latent edges, and a covariate cluster model for node-level attributes. This specification allows anatomical or functional attributes of regions of interest to contribute information when network signals are weak or sparse. We develop an efficient Markov chain Monte Carlo algorithm for posterior sampling and select the number of communities using the widely applicable information criterion, avoiding prior specification of this quantity. Simulation studies demonstrate improved community recovery relative to existing methods across varying noise levels, covariate signal strengths, and numbers of noisy networks, with larger gains when network noise is moderate to high or only a small number of noisy networks is available. Applications to functional brain networks from the Alzheimer's Disease Neuroimaging Initiative and the Human Connectome Project identify biologically interpretable structures and capture disease-related reorganization and individual-level variation.
