论文标题
关于通过内核Stein统计评估图生成器的RKHS选择
On RKHS Choices for Assessing Graph Generators via Kernel Stein Statistics
论文作者
论文摘要
基于得分的核心化的Stein差异（KSD）测试已成为适合拟合测试的强大工具，尤其是在高维度下；但是，测试性能可能取决于在繁殖内核希尔伯特空间（RKHS）中的内核的选择。在这里，我们评估了RKHS选择对随机网络模型的KSD测试的影响，该模型是为Xu and Reinert（2021）中的指数随机图模型（ERGM）以及XU和Reinert中的合成图生成器（2022）开发的。我们在不同情况下研究了测试的功率性能和计算运行时，包括密度和稀疏图制度。在合成和现实世界网络应用程序上显示和讨论了有关模型评估任务内核性能的实验结果。
Score-based kernelised Stein discrepancy (KSD) tests have emerged as a powerful tool for the goodness of fit tests, especially in high dimensions; however, the test performance may depend on the choice of kernels in an underlying reproducing kernel Hilbert space (RKHS). Here we assess the effect of RKHS choice for KSD tests of random networks models, developed for exponential random graph models (ERGMs) in Xu and Reinert (2021)and for synthetic graph generators in Xu and Reinert (2022). We investigate the power performance and the computational runtime of the test in different scenarios, including both dense and sparse graph regimes. Experimental results on kernel performance for model assessment tasks are shown and discussed on synthetic and real-world network applications.
