networksis: A Package to Simulate Bipartite Graphs with Fixed Marginals Through Sequential Importance Sampling
Ryan Admiraal1, Mark S Handcock2
1Department of Statistics, University of Washington, Box 354322, Seattle WA 98195-4332, United States of America.
Journal of Statistical Software
|November 14, 2017
Summary
Simulating graphs with specific properties is crucial for social network analysis. The networksis R package offers efficient simulation and graph generation tools for network analysis tasks.
Area of Science:
- Graph theory
- Network analysis
- Computational statistics
Background:
- Graph simulation is vital for social network analysis.
- Sequential importance sampling is effective for graph property estimation.
- R is a popular statistical software environment.
Purpose of the Study:
- Introduce the networksis R package.
- Demonstrate its utility in graph simulation for network analysis.
- Facilitate estimation of graph counts and null distributions.
Main Methods:
- Utilizing the simulate and simulate_sis functions within the networksis package.
- Applying sequential importance sampling for graph property estimation.
- Generating initial graphs for Markov chain Monte Carlo (MCMC) simulations.
Main Results:
- The networksis package provides efficient graph simulation capabilities.
- Functions can estimate the number of graphs with fixed marginals.
- Null distributions of graph statistics can be accurately estimated.
- The package aids in generating starting graphs for MCMC.
Conclusions:
- The networksis package is a valuable tool for researchers in network analysis.
- It simplifies complex graph simulation and analysis tasks.
- Enhances the study of social networks through improved simulation methods.
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