Fastnet: An r package for fast simulation and analysis of large-scale social networks

Xu Dong; Luis Castro; Nazrul Shaikh

doi:10.18637/jss.v096.i07

Fastnet: An r package for fast simulation and analysis of large-scale social networks

Xu Dong, Luis Castro, Nazrul Shaikh

Research output: Contribution to journal › Article › peer-review

Abstract

Traditional tools and software for social network analysis are seldom scalable and/or fast. This paper provides an overview of an R package called fastnet, a tool for scaling and speeding up the simulation and analysis of large-scale social networks. fastnet uses multi-core processing and sub-graph sampling algorithms to achieve the desired scale-up and speed-up. Simple examples, usages, and comparisons of scale-up and speed-up as compared to other R packages, i.e., igraph and statnet, are presented.

Original language	English (US)
Pages (from-to)	1-23
Number of pages	23
Journal	Journal of Statistical Software
Volume	96
DOIs	https://doi.org/10.18637/jss.v096.i07
State	Published - 2020
Externally published	Yes

Keywords

Multi-core process-ing
Network metrics
Network simulation
Sampling
Social network analysis

ASJC Scopus subject areas

Software
Statistics and Probability
Statistics, Probability and Uncertainty

Access to Document

10.18637/jss.v096.i07

Cite this

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title = "Fastnet: An r package for fast simulation and analysis of large-scale social networks",

abstract = "Traditional tools and software for social network analysis are seldom scalable and/or fast. This paper provides an overview of an R package called fastnet, a tool for scaling and speeding up the simulation and analysis of large-scale social networks. fastnet uses multi-core processing and sub-graph sampling algorithms to achieve the desired scale-up and speed-up. Simple examples, usages, and comparisons of scale-up and speed-up as compared to other R packages, i.e., igraph and statnet, are presented.",

keywords = "Multi-core process-ing, Network metrics, Network simulation, Sampling, Social network analysis",

author = "Xu Dong and Luis Castro and Nazrul Shaikh",

note = "Funding Information: The authors would like to thank Christian Llano Robayo, who has contributed to refining details of fastnet. This research was partially supported by the United States Office of Naval Research (ONR) through grant N00014-10-1-0140. Publisher Copyright: {\textcopyright} 2020, American Statistical Association. All rights reserved. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.",

year = "2020",

doi = "10.18637/jss.v096.i07",

language = "English (US)",

volume = "96",

pages = "1--23",

journal = "Journal of Statistical Software",

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publisher = "University of California at Los Angeles",

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T2 - An r package for fast simulation and analysis of large-scale social networks

AU - Dong, Xu

AU - Castro, Luis

AU - Shaikh, Nazrul

N1 - Funding Information: The authors would like to thank Christian Llano Robayo, who has contributed to refining details of fastnet. This research was partially supported by the United States Office of Naval Research (ONR) through grant N00014-10-1-0140. Publisher Copyright: © 2020, American Statistical Association. All rights reserved. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.

PY - 2020

Y1 - 2020

N2 - Traditional tools and software for social network analysis are seldom scalable and/or fast. This paper provides an overview of an R package called fastnet, a tool for scaling and speeding up the simulation and analysis of large-scale social networks. fastnet uses multi-core processing and sub-graph sampling algorithms to achieve the desired scale-up and speed-up. Simple examples, usages, and comparisons of scale-up and speed-up as compared to other R packages, i.e., igraph and statnet, are presented.

AB - Traditional tools and software for social network analysis are seldom scalable and/or fast. This paper provides an overview of an R package called fastnet, a tool for scaling and speeding up the simulation and analysis of large-scale social networks. fastnet uses multi-core processing and sub-graph sampling algorithms to achieve the desired scale-up and speed-up. Simple examples, usages, and comparisons of scale-up and speed-up as compared to other R packages, i.e., igraph and statnet, are presented.

KW - Multi-core process-ing

KW - Network metrics

KW - Network simulation

KW - Sampling

KW - Social network analysis

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Fastnet: An r package for fast simulation and analysis of large-scale social networks

Abstract

Keywords

ASJC Scopus subject areas

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