Cover Image

An Eigengap Ratio Test for Determining the Number of Communities in Network Data

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Wu, Yujia, Zhang, Jingfei, Lan, Wei, Tsai, Chih-Ling
Format:	Preprint
Published:	2024
Subjects:	Methodology
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

A Goodness-of-Fit Test for Sparse Networks
by: Wu, Yujia, et al.
Published: (2025)

A spectral inference method for determining the number of communities in networks
by: Wu, Yujia, et al.
Published: (2026)

Maximum-of-Differences Test for Comparing Multivariate K-Sample Distributions
by: Lan, Wei, et al.
Published: (2026)

Eigengap Sparsity for Covariance Parsimony
by: Szwagier, Tom, et al.
Published: (2025)

Review on Determining the Number of Communities in Network Data
by: Du, Zhengyuan, et al.
Published: (2025)

Pseudo-Likelihood Ratio Screening based on Network Data with Applications
by: Hu, Wei, et al.
Published: (2025)

High-dimensional covariance regression with application to co-expression QTL detection
by: Kim, Rakheon, et al.
Published: (2024)

A covariate-dependent Cholesky decomposition for high-dimensional covariance regression
by: Kim, Rakheon, et al.
Published: (2026)

Estimating Treatment and Spillover Effects with the Ego-Cluster Experimental Design
by: Liu, Xiao, et al.
Published: (2026)

Jointly Modeling and Clustering Tensors in High Dimensions
by: Cai, Biao, et al.
Published: (2021)

Determining the Number of Common Functional Factors with Twice Cross-Validation
by: Jiang, Hui, et al.
Published: (2024)

Determine the Number of States in Hidden Markov Models via Marginal Likelihood
by: Chen, Yang, et al.
Published: (2024)

Testing Conditional Independence via Density Ratio Regression
by: Ai, Chunrong, et al.
Published: (2025)

Statistical Inference on High Dimensional Gaussian Graphical Regression Models
by: Meng, Xuran, et al.
Published: (2024)

Determining the Number of Communities in Sparse and Imbalanced Settings
by: Shao, Zhixuan, et al.
Published: (2024)

Modeling Non-Uniform Hypergraphs Using Determinantal Point Processes
by: Chen, Yichao, et al.
Published: (2025)

Multiple Hypothesis Testing To Estimate The Number Of Communities in Stochastic Block Models
by: Jha, Chetkar, et al.
Published: (2025)

Sequential Test for Practical Significance: Truncated Mixture Sequential Probability Ratio Test
by: Shim, Kyu Min
Published: (2025)

Testing Hypotheses About Ratios of Linear Trend Slopes in Systems of Equations with a Focus on Tests of Equal Trend Ratios
by: Vogelsang, Timothy J.
Published: (2026)

Generalized Tensor Completion with Non-Random Missingness
by: Zhang, Maoyu, et al.
Published: (2025)

Limiting laws and consistent estimation criteria for fixed and diverging number of spiked eigenvalues
by: Hu, Jianwei, et al.
Published: (2020)

Correlation Improves Group Testing: Modeling Concentration-Dependent Test Errors
by: Wan, Jiayue, et al.
Published: (2021)

A Preferential Latent Space Model for Text Networks
by: Zhang, Maoyu, et al.
Published: (2024)

Incorporating external data for analyzing randomized clinical trials: A transfer learning approach
by: Gu, Yujia, et al.
Published: (2024)

Order Determination for Functional Data
by: Zhang, Chi, et al.
Published: (2025)

Uniform Critical Values for Likelihood Ratio Tests in Boundary Problems
by: Cavaliere, Giuseppe, et al.
Published: (2025)

Bi-Gaussian Mirrors for False Discovery Rate Control
by: Wu, Yujia, et al.
Published: (2026)

Is 1:1 Always Most Powerful? Why Careful Determination of Allocation Ratios Matters in Trial Design
by: Pin, Lukas, et al.
Published: (2025)

Projection Pursuit Density Ratio Estimation
by: Wang, Meilin, et al.
Published: (2025)

OPTICS: Order-Preserved Test-Inverse Confidence Set for Number of Change-Points
by: Sun, Ao, et al.
Published: (2026)

Computational and Numerical Properties of a Broadband Subspace-Based Likelihood Ratio Test
by: Pahalson, Cornelius A. H., et al.
Published: (2024)

Testing the Homogeneity of Proportions for Correlated Bilateral Data via the Clayton Copula
by: Liang, Shuyi, et al.
Published: (2025)

D-optimal Design for Network A/B Testing
by: Pokhiko, Victoria, et al.
Published: (2019)

A Generalized Fréchet Test for Object Data with Unequal Repeated Measurements
by: Zhang, Jingru, et al.
Published: (2025)

On the Number of Conditional Independence Tests in Constraint-based Causal Discovery
by: Monés, Marc Franquesa, et al.
Published: (2026)

Neural Network Machine Regression (NNMR): A Deep Learning Framework for Uncovering High-order Synergistic Effects
by: Zhang, Jiuchen, et al.
Published: (2026)

Correcting a Nonparametric Two-sample Graph Hypothesis Test for Graphs with Different Numbers of Vertices with Applications to Connectomics
by: Alyakin, Anton A., et al.
Published: (2020)

Assumption-lean covariate adjustment under covariate adaptive randomization when $p = o (n)$
by: Gu, Yujia, et al.
Published: (2025)

Estimation of the Number Needed to Treat, the Number Needed to be Exposed, and the Exposure Impact Number with Instrumental Variables
by: Vancak, Valentin, et al.
Published: (2023)

Group Sequential Methods for the Win Ratio
by: Bergemann, Tracy, et al.
Published: (2026)