CONTEST: A controllable test toolbox for MATLAB. Large, sparse networks that describe complex interactions are a common feature across a number of disciplines, giving rise to many challenging matrix computational tasks. Several random graph models have been proposed that capture key properties of real-life networks. These models provide realistic, parametrized matrices for testing linear system and eigenvalue solvers. CONTEST (CONtrollable TEST matrices) is a random network toolbox for MATLAB that implements nine models. The models produce unweighted directed or undirected graphs; that is, symmetric or unsymmetric matrices with elements equal to zero or one. They have one or more parameters that affect features such as sparsity and characteristic pathlength and all can be of arbitrary dimension. Utility functions are supplied for rewiring, adding extra shortcuts and subsampling in order to create further classes of networks. Other utilities convert the adjacency matrices into real-valued coefficient matrices for naturally arising computational tasks that reduce to sparse linear system and eigenvalue problems.

This software is also peer reviewed by journal TOMS.

References in zbMATH (referenced in 22 articles )

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  1. Boito, Paola; Eidelman, Yuli; Gemignani, Luca: Computing the reciprocal of a (\phi)-function by rational approximation (2022)
  2. Guo, Yongyan; Wu, Gang: A new lower bound on the size of the smallest vertex separator of a graph (2022)
  3. Higham, Nicholas J.; Mikaitis, Mantas: Anymatrix: an extensible MATLAB matrix collection (2022)
  4. Higham, Desmond J.; Mantzaris, Alexander V.: A network model for polarization of political opinion (2020)
  5. Arrigo, Francesca; Grindrod, Peter; Higham, Desmond J.; Noferini, Vanni: On the exponential generating function for non-backtracking walks (2018)
  6. Grindrod, Peter; Higham, Desmond J.; Noferini, Vanni: The deformed graph Laplacian and its applications to network centrality analysis (2018)
  7. Benzi, Michele; Simoncini, Valeria: Approximation of functions of large matrices with Kronecker structure (2017)
  8. Fenu, Caterina; Higham, Desmond J.: Block matrix formulations for evolving networks (2017)
  9. Fika, Paraskevi; Mitrouli, Marilena: Aitken’s method for estimating bilinear forms arising in applications (2017)
  10. Arrigo, Francesca; Benzi, Michele: Updating and downdating techniques for optimizing network communicability (2016)
  11. Arioli, Mario; Duff, Iain S.: Preconditioning linear least-squares problems by identifying a basis matrix (2015)
  12. Shao, Meiyue; Gao, Weiguo; Xue, Jungong: Aggressively truncated Taylor series method for accurate computation of exponentials of essentially nonnegative matrices (2014)
  13. Benzi, Michele; Kuhlemann, Verena: Chebyshev acceleration of the GeneRank algorithm (2013)
  14. Betcke, Timo; Higham, Nicholas J.; Mehrmann, Volker; Schröder, Christian; Tisseur, Françoise: NLEVP, a collection of nonlinear eigenvalue problems (2013)
  15. Fenu, C.; Martin, D.; Reichel, L.; Rodriguez, G.: Network analysis via partial spectral factorization and Gauss quadrature (2013)
  16. Wu, Gang; Xu, Wei; Zhang, Ying; Wei, Yimin: A preconditioned conjugate gradient algorithm for GeneRank with application to microarray data mining (2013)
  17. Wu, Gang; Zhang, Ying; Wei, Yimin: Accelerating the Arnoldi-type algorithm for the PageRank problem and the ProteinRank problem (2013)
  18. Grindrod, Peter; Higham, Desmond J.: Models for evolving networks: with applications in telecommunication and online activities (2012)
  19. Davis, Timothy A.; Hu, Yifan: The University of Florida sparse matrix collection (2011)
  20. Benzi, Michele; Boito, Paola: Quadrature rule-based bounds for functions of adjacency matrices (2010)

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