Sparse networks with core-periphery structure

DOI10.1214/21-EJS1819zbMath1470.05149arXiv1910.09679MaRDI QIDQ2044362

Judith Rousseau, Cian Naik, François Caron

Publication date: 9 August 2021

Published in: Electronic Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1910.09679

zbMATH Keywords

networksrandom graphspoint processesBayesian nonparametricssparsityPoisson random measurescompletely random measures

Mathematics Subject Classification ID

Bayesian inference (62F15) Random graphs (graph-theoretic aspects) (05C80) Point processes (e.g., Poisson, Cox, Hawkes processes) (60G55) Density (toughness, etc.) (05C42)

Related Items

Core-periphery structure in networks: a statistical exposition

Uses Software

• plfit

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Bayesian nonparametric Plackett-Luce models for the analysis of preferences for college degree programmes
• On the posterior distribution of classes of random means
• A modified Kolmogorov-Smirnov test sensitive to tail alternatives
• Estimation and prediction for stochastic blockmodels for graphs with latent block structure
• Distributional results for means of normalized random measures with independent increments
• Inference from iterative simulation using multiple sequences
• Bootstrap estimators for the tail-index and for the count statistics of graphex processes
• Completely random measures
• Power-Law Distributions in Empirical Data
• Estimation and Prediction for Stochastic Blockstructures
• Detection of core–periphery structure in networks using spectral methods and geodesic paths
• Sparse Graphs Using Exchangeable Random Measures
• Exchangeable Random Measures for Sparse and Modular Graphs with Overlapping Communities
• Controlling the Reinforcement in Bayesian Non-Parametric Mixture Models
• Core-Periphery Structure in Networks (Revisited)
• Compound Random Measures and their Use in Bayesian Non-Parametrics
• Networks