We are interested in the problem of two-sample network hypothesis testing: given two networks with the same set of nodes, we wish to test whether the underlying Bernoulli probability matrices of the two networks are the same or not. We propose Interlacing Balance Measure (IBM) as a new two-sample testing approach. We consider the {\it Degree-Corrected Mixed-Membership (DCMM)} model for undirected networks, where we allow severe degree heterogeneity, mixed-memberships, flexible sparsity levels, and weak signals. In such a broad setting, how to find a test that has a tractable limiting null and optimal testing performances is a challenging problem. We show that IBM is such a test: in a broad DCMM setting with only mild regularity conditions, IBM has $N(0,1)$ as the limiting null and achieves the optimal phase transition.
While the above is for undirected networks, IBM is a unified approach and is directly implementable for directed networks. For a broad directed-DCMM (extension of DCMM for directed networks) setting, we show that IBM has $N(0, 1/2)$ as the limiting null and continues to achieve the optimal phase transition. We have also applied IBM to the Enron email network and a gene co-expression network, with interesting results.
Refined thresholds for inconsistency: The effect of the graph associated with incomplete pairwise comparisons
Corvinus University of Budapest (BCE), Institute of Operations and Decision Sciences, Department of Operations Research and Actuarial Sciences（布达佩斯考文纽斯大学）；Institute for Computer Science and Control (SZTAKI), Hungarian Research Network (HUN-REN)（匈牙利国家研究网络计算机科学与控制研究所）
Size independence of consistency index for pairwise comparison matrices in analytic hierarchy process
Faculty of Science and Engineering, Otemon Gakuin University（帝塚山学院大学理工学部）；Faculty of Applied Information Science, Hiroshima Institute of Technology（广岛工业大学应用信息科学部）
