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network: A Package for Managing Relational Data in R

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  • Butts, Carter T.

Abstract

Effective memory structures for relational data within R must be capable of representing a wide range of data while keeping overhead to a minimum. The network package provides an class which may be used for encoding complex relational structures composed a vertex set together with any combination of undirected/directed, valued/unvalued, dyadic/hyper, and single/multiple edges; storage requirements are on the order of the number of edges involved. Some simple constructor, interface, and visualization functions are provided, as well as a set of operators to facilitate employment by end users. The package also supports a C-language API, which allows developers to work directly with network objects within backend code.

Suggested Citation

  • Butts, Carter T., 2008. "network: A Package for Managing Relational Data in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 24(i02).
    • Handle: RePEc:jss:jstsof:v:024:i02
    DOI: http://hdl.handle.net/10.18637/jss.v024.i02
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    Cited by:

    1. Johannes Pol, 2019. "Introduction to Network Modeling Using Exponential Random Graph Models (ERGM): Theory and an Application Using R-Project," Computational Economics, Springer;Society for Computational Economics, vol. 54(3), pages 845-875, October.
    2. Olivier Sire & Salim Lardjane, 2014. "VORTEX, a dedicated tool for the development of territorial intelligence based on a systemic approach of the social network for innovation in Brittany [VORTEX, un outil d'intelligence territoriale ," Working Papers hal-01098549, HAL.
    3. Caimo, Alberto & Friel, Nial, 2014. "Bergm: Bayesian Exponential Random Graphs in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 61(i02).
    4. Bjarne Larsen & Kyle Gardner & Carsten Pedersen & Marian Ørgaard & Zoë Migicovsky & Sean Myles & Torben Bo Toldam-Andersen, 2018. "Population structure, relatedness and ploidy levels in an apple gene bank revealed through genotyping-by-sequencing," PLOS ONE, Public Library of Science, vol. 13(8), pages 1-14, August.
    5. Paula Ianishi & Oilson Alberto Gonzatto Junior & Marcos Jardel Henriques & Diego Carvalho do Nascimento & Gabriel Kamada Mattar & Pedro Luiz Ramos & Anderson Ara & Francisco Louzada, 2022. "Probability on Graphical Structure: A Knowledge-Based Agricultural Case," Annals of Data Science, Springer, vol. 9(2), pages 327-345, April.
    6. Mengyu Yu & Mazie Krehbiel & Samantha Thompson & Tatjana Miljkovic, 2020. "An exploration of gender gap using advanced data science tools: actuarial research community," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(2), pages 767-789, May.
    7. Groenen, Patrick J. F. & van de Velden, Michel, 2016. "Multidimensional Scaling by Majorization: A Review," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 73(i08).
    8. repec:jss:jstsof:24:i07 is not listed on IDEAS
    9. Bender-deMol, Skye & Morris, Martina & Moody, James, 2008. "Prototype Packages for Managing and Animating Longitudinal Network Data: dynamicnetwork and rSoNIA," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 24(i07).
    10. Milad Abbasiharofteh & Tom Broekel, 2021. "Still in the shadow of the wall? The case of the Berlin biotechnology cluster," Environment and Planning A, , vol. 53(1), pages 73-94, February.
    11. Johannes VAN DER POL, 2016. "The modelling of networks using Exponential Random Graph Models: an introduction," Cahiers du GREThA (2007-2019) 2016-22, Groupe de Recherche en Economie Théorique et Appliquée (GREThA).
    12. Shulgin, Sergey & Zinkina, Julia & Korotayev, Andrey, 2017. "“Neighbors in values”: A new dataset of cultural distances between countries based on individuals’ values, and its application to the study of global trade," Research in International Business and Finance, Elsevier, vol. 42(C), pages 966-985.
    13. Georgia Pollard & James Ward & Philip Roetman, 2018. "Typically Diverse: The Nature of Urban Agriculture in South Australia," Sustainability, MDPI, vol. 10(4), pages 1-18, March.
    14. Alfonso Langle-Flores & Zinthia López-Vázquez & Rosa María Chávez-Dagostino & Adriana Aguilar-Rodríguez, 2022. "COVID-19 Impacts on Whale-Watching Collaboration Networks," Sustainability, MDPI, vol. 14(21), pages 1-14, October.
    15. Vasaf, Esmaeil & Sanatkhani, Mahboobeh, 2014. "Dynamic of Publication Network in German Photovoltaic Industry," MPRA Paper 65453, University Library of Munich, Germany.
    16. Francesca Mateo & Zhengcheng He & Lin Mei & Gorka Ruiz de Garibay & Carmen Herranz & Nadia García & Amanda Lorentzian & Alexandra Baiges & Eline Blommaert & Antonio Gómez & Oriol Mirallas & Anna Garri, 2022. "Modification of BRCA1-associated breast cancer risk by HMMR overexpression," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
    17. Juan D Montoro-Pons & Manuel Cuadrado-García, 2021. "Analyzing online search patterns of music festival tourists," Tourism Economics, , vol. 27(6), pages 1276-1300, September.
    18. Elina H. Hwang & David Krackhardt, 2020. "Online Knowledge Communities: Breaking or Sustaining Knowledge Silos?," Production and Operations Management, Production and Operations Management Society, vol. 29(1), pages 138-155, January.
    19. Johannes van Der Pol, 2017. "Introduction to network modeling using Exponential Random Graph models (ERGM)," Working Papers hal-01284994, HAL.
    20. Hunter, David R. & Goodreau, Steven M. & Handcock, Mark S., 2013. "ergm.userterms: A Template Package for Extending statnet," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 52(i02).
    21. Suesse Thomas & Chambers Ray, 2018. "Using Social Network Information for Survey Estimation," Journal of Official Statistics, Sciendo, vol. 34(1), pages 181-209, March.

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