Generating General Preferential Attachment Networks with R Package wdnet
Volume 21, Issue 3 (2023): Special Issue: Advances in Network Data Science, pp. 538–556
Pub. online: 25 July 2023
Type: Computing In Data Science
Open Access
Open Access
Received
19 May 2023
19 May 2023
Accepted
5 July 2023
5 July 2023
Published
25 July 2023
25 July 2023
Abstract
Preferential attachment (PA) network models have a wide range of applications in various scientific disciplines. Efficient generation of large-scale PA networks helps uncover their structural properties and facilitate the development of associated analytical methodologies. Existing software packages only provide limited functions for this purpose with restricted configurations and efficiency. We present a generic, user-friendly implementation of weighted, directed PA network generation with R package wdnet. The core algorithm is based on an efficient binary tree approach. The package further allows adding multiple edges at a time, heterogeneous reciprocal edges, and user-specified preference functions. The engine under the hood is implemented in C++. Usages of the package are illustrated with detailed explanation. A benchmark study shows that wdnet is efficient for generating general PA networks not available in other packages. In restricted settings that can be handled by existing packages, wdnet provides comparable efficiency.
Supplementary material
Supplementary Materials
(1)
The code used for benchmarks and the R Markdown source for the paper can be found at https://github.com/Yelie-Yuan/code-sharing/tree/main/generating-pa.
(2)
The development version of the package is available at https://gitlab.com/wdnetwork/wdnet.
References
Atwood J, Ribeiro B, Towsley D (2015). Efficient network generation under general preferential attachment. Computational Social Networks, 2(1): 7. https://doi.org/10.1186/s40649-015-0012-9
Barabási AL, Albert R (1999). Emergence of scaling in random networks. Science, 286(5439): 509–512. https://doi.org/10.1126/science.286.5439.509
Barrat A, Barthélemy M, Vespignani A (2004). Weighted evolving networks: Coupling topology and weight dynamics. Physical Review Letters, 92(22): 228701. https://doi.org/10.1103/PhysRevLett.92.228701
Britton T (2020). Directed preferential attachment models: Limiting degree distributions and their tails. Journal of Applied Probability, 57(1): 122–136. https://doi.org/10.1017/jpr.2019.80
Capocci A, Servedio VDP, Colaiori F, Buriol LS, Donato D, Leonardi S, et al. (2006). Preferential attachment in the growth of social networks: The internet encyclopedia Wikipedia. Physical Review E, 74(3): 036116. https://doi.org/10.1103/PhysRevE.74.036116
Dong X, Castro L, Shaikh N (2020). fastnet: An R package for fast simulation and analysis of large-scale social networks. Journal of Statistical Software, 96(7): 1–23. https://doi.org/10.18637/jss.v096.i07
Eddelbuettel D, François R (2011). Rcpp: Seamless R and C++ integration. Journal of Statistical Software, 40(8): 1–18. https://doi.org/10.18637/jss.v040.i08
Erdös P, Rényi A (1959). On random graphs I. Publicationes Mathematicae Debrecen, 6: 290–297. https://doi.org/10.5486/PMD.1959.6.3-4.12
Gilbert EN (1959). Random graphs. Annals of Mathematical Statistics, 30(4): 1141–1144. https://doi.org/10.1214/aoms/1177706098
Kong JS, Sarshar N, Roychowdhury VP (2008). Experience versus talent shapes the structure of the web. Proceedings of the National Academy of Sciences, 105(37): 13724–13729. https://doi.org/10.1073/pnas.0805921105
Pham T, Sheridan P, Shimodaira H (2020). PAFit: An R package for the non-parametric estimation of preferential attachment and node fitness in temporal complex networks. Journal of Statistical Software, 92(3): 1–30. https://doi.org/10.18637/jss.v092.i03
Wang T, Resnick SI (2022a). Asymptotic dependence of in-and out-degrees in a preferential attachment model with reciprocity. Extremes, 25(3): 417–450. https://doi.org/10.1007/s10687-022-00439-5
Wang T, Resnick SI (2023). Poisson edge growth and preferential attachment networks. Methodology and Computing in Applied Probability, 25(1): 8. https://doi.org/10.1007/s11009-023-09997-y
Wang T, Yan J, Yuan Y, Zhang P (2022). Generating directed networks with predetermined assortativity measures. Statistics and Computing, 32(5): 91. https://doi.org/10.1007/s11222-022-10161-8
Yuan Y, Yan J, Zhang P (2021). Assortativity measures for weighted and directed networks. Journal of Complex Networks, 9(2): cnab017. https://doi.org/10.1093/comnet/cnab017
