Network dependence testing via diffusion maps and distance-based correlations
Network dependence testing via diffusion maps and distance-based correlations
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DOI：
10.1093/biomet/asz045
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发表时间：
2019-12-01
期刊：
影响因子：
2.7
通讯作者：
Vogelstein, Joshua T.
中科院分区：
文献类型：
--
作者：
Lee, Youjin;Shen, Cencheng;Vogelstein, Joshua T.
网络连通性与节点属性之间的关系是网络科学的核心问题之一。网络的依赖结构和高维性在理论保证和实证表现方面对传统的依赖测试提出了独特的挑战。我们提出了一种通过扩散图和基于距离的相关性来测试网络依赖性的方法。我们证明了新方法在图结构的温和分布假设下产生一致的检验统计量，并证明了它能够有效地识别最具信息量的图嵌入相对于扩散时间。模拟和真实的数据的方法说明。
Deciphering the associations between network connectivity and nodal attributes is one of the core problems in network science. The dependency structure and high dimensionality of networks pose unique challenges to traditional dependency tests in terms of theoretical guarantees and empirical performance. We propose an approach to test network dependence via diffusion maps and distance-based correlations. We prove that the new method yields a consistent test statistic under mild distributional assumptions on the graph structure, and demonstrate that it is able to efficiently identify the most informative graph embedding with respect to the diffusion time. The methodology is illustrated on both simulated and real data.