Are We There Yet? When to Stop a Markov Chain while Generating Random Graphs

DOI10.1007/978-3-642-30541-2_12zbMath1342.05144arXiv1202.3473OpenAlexW2117863112MaRDI QIDQ2900593

Jaideep Ray, C. Seshadhri, Ali Pınar

Publication date: 23 July 2012

Published in: Lecture Notes in Computer Science (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1202.3473

zbMATH Keywords

Monte Carlo methodsMarkov chainindependent samplesgraph generation

Mathematics Subject Classification ID

Random graphs (graph-theoretic aspects) (05C80) Markov chains (discrete-time Markov processes on discrete state spaces) (60J10)

Related Items (4)

Are We There Yet? When to Stop a Markov Chain while Generating Random Graphs ⋮ I/O-Efficient Generation of Massive Graphs Following the LFR Benchmark ⋮ Unnamed Item ⋮ Fast Sequential Creation of Random Realizations of Degree Sequences

Cites Work

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• Fast uniform generation of regular graphs
• Inference from iterative simulation using multiple sequences
• Are We There Yet? When to Stop a Markov Chain while Generating Random Graphs
• A polynomial-time approximation algorithm for the permanent of a matrix with nonnegative entries
• Emergence of Scaling in Random Networks
• Sampling Graphs with a Prescribed Joint Degree Distribution Using Markov Chains
• Collective dynamics of ‘small-world’ networks
• Discrete Multivariate Analysis Theory and Practice

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