论文标题
大规模内核Granger因果关系推断出有针对性图的拓扑，并应用于大脑网络
Large-scale kernelized GRANGER causality to infer topology of directed graphs with applications to brain networks
论文作者
论文摘要
通过共同发展和相互作用时间序列的网络过程的图形拓扑推断对于网络研究至关重要。向量自回旋模型（VAR）是有向图的拓扑推理的流行方法；但是，在较短时间序列的大型网络中，拓扑估计变得不足。本文提出了一种新型的非线性保护拓扑推断方法，用于与共同发展的淋巴结过程，该方法解决了不良性问题。提出的大规模内核Granger因果关系（LSKGC）使用内核功能将数据转换为低维特征空间并解决特征空间中的自回归问题，然后找到输入空间中的前图像来推断拓扑。与现有方法相比，对具有非线性和线性依赖性和已知地面的合成数据集的大量模拟表明，接收器操作器操作特征的接收器操作特征（AUC）与现有方法相比，该面积有显着改善。此外，功能性磁共振成像（fMRI）研究对实际数据集进行测试表明，精神分裂症患者的诊断任务的准确性为96.3％，这在文献中最高的是只有大脑时间序列信息。
Graph topology inference of network processes with co-evolving and interacting time-series is crucial for network studies. Vector autoregressive models (VAR) are popular approaches for topology inference of directed graphs; however, in large networks with short time-series, topology estimation becomes ill-posed. The present paper proposes a novel nonlinearity-preserving topology inference method for directed networks with co-evolving nodal processes that solves the ill-posedness problem. The proposed method, large-scale kernelized Granger causality (lsKGC), uses kernel functions to transform data into a low-dimensional feature space and solves the autoregressive problem in the feature space, then finds the pre-images in the input space to infer the topology. Extensive simulations on synthetic datasets with nonlinear and linear dependencies and known ground-truth demonstrate significant improvement in the Area Under the receiver operating characteristic Curve ( AUC ) of the receiver operating characteristic for network recovery compared to existing methods. Furthermore, tests on real datasets from a functional magnetic resonance imaging (fMRI) study demonstrate 96.3 percent accuracy in diagnosis tasks of schizophrenia patients, which is the highest in the literature with only brain time-series information.
