网络的同时预测和社区检测及其在神经成像中的应用
Simultaneous prediction and community detection for networks with application to neuroimaging
摘要：
网络中的社区结构在许多不同的领域都可以观察到，无监督社区检测在文献中受到了很多关注。网络分析的重点越来越多地转向在其他预测或推理任务中使用网络信息，而不仅仅是分析网络本身。特别是，在神经成像应用中，大脑网络可用于多个受试者，目标通常是预测感兴趣的表型。众所周知，社区结构是大脑网络的一个特征，通常对应于负责不同功能的大脑不同区域。大脑有标准的包裹到这些区域，通常通过对健康受试者的大脑连接体应用聚类方法来获得。然而，当目标是预测表型或区分不同的条件时，来自一组无关的健康受试者的这些静态社区可能不是最有用的预测。在这里，我们提出了一种监督社区检测的方法，旨在找到一个分区的网络到社区是最有用的预测一个特定的反应。我们使用块结构的正则化惩罚与预测损失函数相结合，并计算与频谱方法和ADMM优化算法相结合的解决方案。我们表明，谱聚类方法恢复正确的社区下加权随机块模型。该方法在模拟和真实大脑网络上都表现良好，为任务依赖性大脑区域的想法提供了支持。
英文摘要：
Community structure in networks is observed in many different domains, and unsupervised community detection has received a lot of attention in the literature. Increasingly the focus of network analysis is shifting towards using network information in some other prediction or inference task rather than just analyzing the network itself. In particular, in neuroimaging applications brain networks are available for multiple subjects and the goal is often to predict a phenotype of interest. Community structure is well known to be a feature of brain networks, typically corresponding to different regions of the brain responsible for different functions. There are standard parcellations of the brain into such regions, usually obtained by applying clustering methods to brain connectomes of healthy subjects. However, when the goal is predicting a phenotype or distinguishing between different conditions, these static communities from an unrelated set of healthy subjects may not be the most useful for prediction. Here we present a method for supervised community detection, aiming to find a partition of the network into communities that is most useful for predicting a particular response. We use a block-structured regularization penalty combined with a prediction loss function, and compute the solution with a combination of a spectral method and an ADMM optimization algorithm. We show that the spectral clustering method recovers the correct communities under a weighted stochastic block model. The method performs well on both simulated and real brain networks, providing support for the idea of task-dependent brain regions.
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