论文标题
具有未知链接功能的潜在距离图上的两样本测试
Two-sample Testing on Latent Distance Graphs With Unknown Link Functions
论文作者
论文摘要
我们提出了一个有效且一致的测试，以假设同一顶点集上的两个潜在距离随机图具有相同的生成潜在位置，直到某些无法识别的相似性转换。我们的测试统计量是基于首先通过截断每个人群中平均邻接矩阵的奇异值分解，然后计算这些估计之间的Spearman秩相关系数来估算边缘概率矩阵。对模拟数据的实验结果表明，即使每个人群中只有一个样本，但前面的顶点数量不太小，测试程序即使只有一个样本。在神经连接组图的数据集上的应用表明，我们可以区分不同年龄组的扫描，而在癫痫记录的数据集上应用表明我们可以区分癫痫发作和非癫痫发作事件。
We propose a valid and consistent test for the hypothesis that two latent distance random graphs on the same vertex set have the same generating latent positions, up to some unidentifiable similarity transformations. Our test statistic is based on first estimating the edge probabilities matrices by truncating the singular value decompositions of the averaged adjacency matrices in each population and then computing a Spearman rank correlation coefficient between these estimates. Experimental results on simulated data indicate that the test procedure has power even when there is only one sample from each population, provided that the number of vertices is not too small. Application on a dataset of neural connectome graphs showed that we can distinguish between scans from different age groups while application on a dataset of epileptogenic recordings showed that we can discriminate between seizure and non-seizure events.
