web_matrix, a Python code which stores sample matrices describing a web page network. These matrices are typically very sparse, and the examples here are stored using the sparse triplet (ST) format. They can be used to demonstrate pagerank and other graph algorithms.


The computer code and data files made available on this web page are distributed under the MIT license


web_matrix is available in a MATLAB version and an Octave version and a Python version.

Related Data and Programs:

jordan_matrix, a Python code which returns a random matrix in Jordan canonical form.

levenshtein_matrix, a Python code which returns the Levenshtein distance matrix defined by two strings.

monopoly_matrix, a Python code which computes the adjacency and transition matrices for the game of Monopoly.

pagerank, a Python code which illustrates the eigenvalue (power method) and surfer (Markov chain) approaches to ranking web pages.

plasma_matrix, a Python code which sets up a matrix associated with a problem in plasma physics.

power_method, a Python code which carries out the power method for finding a dominant eigenvalue and its eigenvector.

risk_matrix, a Python code which computes the transition and adjacency matrix for the game of RISK.

snakes_matrix, a Python code which computes the transition matrix for Snakes and Ladders.

sparse_test, a Python code which illustrates the use of sparse matrix utilities;

st, a data directory which illustrates the sparse triplet (ST) format for storing sparse matrices.

test_matrix, a Python code which defines test matrices for which the condition number, determinant, eigenvalues, eigenvectors, inverse, null vectors, P*L*U factorization or linear system solution are known. Examples include the Fibonacci, Hilbert, Redheffer, Vandermonde, Wathen and Wilkinson matrices.

wathen_matrix, a Python code which compares storage schemes (full, banded, sparse triplet, sparse) and solution strategies (A\x, linpack, conjugate gradient (CG)) for linear systems involving the Wathen matrix, which can arise when solving a problem using the finite element method (FEM).


  1. John MacCormick,
    Nine Algorithms That Changed the Future: The Ingenious Ideas that Drive Today's Computers,
    Princeton University Press,
    ISBN-13: 978-0691158198.
  2. Cleve Moler,
    Experiments with Matlab,
    Chapter 7: Google PageRank
  3. Xindong Wu, Vipin Kumar, J Ross Quinlan, Joydeep Ghosh, Qiang Yang, Hiroshi Motoda, Geoffrey McLachlan, Angus Ng, Bing Lu, Philip Yu, Zhi-Hua Zhou, Michael Steinbach, David Hand, Dan Steinberg,
    Top 10 algorithms in data mining,
    Knowledge and Information Systems,
    Volume 14, Number 1, January 2008, pages 1-37.

Source Code:

Last modified on 27 September 2022.