论文标题
在混乱中丢失：在存在错误网络顶点标签的情况下测试功率
Lost in the Shuffle: Testing Power in the Presence of Errorful Network Vertex Labels
论文作者
论文摘要
两样本网络假设检验是一项重要的推论任务，具有跨不同领域的应用，例如医学，神经科学和社会学。这些测试方法中的许多是在隐式假设下运行的，即跨网络的顶点对应关系是先验的。这个假设通常是不真实的，当网络跨网络存在未对准/标记的降低顶点时，随后的测试的功能可能会降低。理论上，基于估计的边缘概率矩阵之间的Frobenius Norm差异或邻接矩阵之间的Frobenius Norm差异，在理论上探索了由于随机点产品和随机块模型网络的背景，从理论上探索了这种损失。在随机块模型和随机点产品图模型中，通过大量的模拟和实验进一步增强了测试能力的损失，其中考虑了文献中多个最近提出的测试的功率损失。最后，在神经科学和社交网络分析中的一对示例中，证明了改组对实际数据测试的影响。
Two-sample network hypothesis testing is an important inference task with applications across diverse fields such as medicine, neuroscience, and sociology. Many of these testing methodologies operate under the implicit assumption that the vertex correspondence across networks is a priori known. This assumption is often untrue, and the power of the subsequent test can degrade when there are misaligned/label-shuffled vertices across networks. This power loss due to shuffling is theoretically explored in the context of random dot product and stochastic block model networks for a pair of hypothesis tests based on Frobenius norm differences between estimated edge probability matrices or between adjacency matrices. The loss in testing power is further reinforced by numerous simulations and experiments, both in the stochastic block model and in the random dot product graph model, where the power loss across multiple recently proposed tests in the literature is considered. Lastly, the impact that shuffling can have in real-data testing is demonstrated in a pair of examples from neuroscience and from social network analysis.
