Assessing the statistical significance of network patterns is crucial for understanding whether such patterns indicate the presence of interesting network phenomena, or whether they simply result from less interesting processes, such as nodal-heterogeneity. Typically, significance is computed with reference to a null model. While there has been extensive research into such null models for unweighted graphs, little has been done for the weighted case. This article suggests a null model for weighted graphs. The model fixes node strengths exactly, and approximately fixes node degrees. A novel MCMC algorithm is proposed for sampling the model, and its stochastic stability is considered. We show empirically that the model compares favorably to alternatives, particularly when network patterns are subtle. We show how the algorithm can be used to evaluate the statistical significance of community structure.
Minimum Risk Equivariant Estimation of the Parameters of the General Half-Normal Distribution by Means of a Monte Carlo Method to Approximate Conditional Expectations
Uniform Convergence of Generalized Conditional Fréchet Means with Applications to Weighted Fréchet Aggregation and Exceedance Set Estimation
Research School of Finance, Actuarial Studies and Statistics, Australian National University（澳大利亚国立大学金融、精算与统计研究学院）；Department of Actuarial Studies and Business Analytics, Macquarie University（麦考瑞大学精算与商业分析系）
A Monte Carlo Method to Approximate Conditional Expectations based on a Theorem of Besicovitch: Application to Equivariant Estimation of the Parameters of the General Half-Normal Distribution
Robust State-Conditional Feature-Weighted Jump Models for Temporal Clustering
University of Milan Department of Economics Management and Quantitative Methods（米兰大学经济学管理与定量方法系）；University of Rome "Tor Vergata" Department of Enterprise Engineering（罗马"托尔维加塔"大学企业工程系）；National Research Council of Italy Institute for Applied Mathematics and Information Technologies（意大利国家研究委员会应用数学与信息技术研究所）
Probabilistic Conformal Prediction with Approximate Conditional Validity
Lagrange Mathematics and Computing Research Center（拉格朗日数学与计算研究中心）；Skolkovo Institute of Science and Technology（斯科尔科沃科学技术研究所）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Ecole Polytechnique（巴黎综合理工学院）
Noise-Compensated, Bias-Corrected Diffusion Weighted Endorectal Magnetic Resonance Imaging via a Stochastically Fully-Connected Joint Conditional Random Field Model
University of Waterloo（滑铁卢大学）；University of Toronto（多伦多大学）；Sunnybrook Research Institute（森尼布鲁克研究所）
