Graph examples from the SuiteSparse Matrix Collection

The six matrices in this directory are unchanged Matrix Market text files, compressed with gzip, downloaded on 2026-09-23 from the University of Florida mirror of the SuiteSparse Matrix Collection. The Les Miserables vertex-name file is also unchanged. Each matrix retains its original metadata header.

Source Original authors Editors
HB/dwt_66 G. Everstine, D. Taylor I. Duff, R. Grimes, J. Lewis
Newman/lesmis D. Knuth M. Newman
HB/dwt_307 G. Everstine, D. Taylor I. Duff, R. Grimes, J. Lewis
HB/494_bus D. Tylavsky I. Duff, R. Grimes, J. Lewis
HB/dwt_1005 G. Everstine, D. Taylor I. Duff, R. Grimes, J. Lewis
HB/1138_bus D. Tylavsky I. Duff, R. Grimes, J. Lewis

Source archives: https://www.cise.ufl.edu/research/sparse/MM/GROUP/NAME.tar.gz, using the group/name in the table. The current Texas A&M HTTPS file server was unreachable during this import; the collection’s original Florida mirror was used.

License and attribution

SuiteSparse distributes the matrices under Creative Commons Attribution 4.0 International, as stated in the collection’s license policy. This license applies to these matrices, their metadata and the derived graphs returned by suitesparse.graph(), separately from grip’s software license.

Please credit the original authors above and cite Davis, T. A. and Hu, Y. (2011), The University of Florida Sparse Matrix Collection, ACM Transactions on Mathematical Software 38(1), Article 1, https://doi.org/10.1145/2049662.2049663. For Les Miserables, also cite Knuth, D. E. (1993), The Stanford GraphBase: A Platform for Combinatorial Computing, Addison-Wesley. The complete source-specific notes remain in each matrix header.

The examples were selected from Zheng, J. X., Pawar, S. and Goodman, D. F. M. (2018), Graph Drawing by Stochastic Gradient Descent, https://doi.org/10.1109/TVCG.2018.2859997.

Derived graphs

data-raw/original_six.R reconstructs the six examples offline from these files as part of data-raw/build-suitesparse.py. Derived names end in _unweighted_graph. The conversion removes diagonal entries, discards explicit zeros, combines symmetric/duplicate edges, and assigns unit traversal lengths. All vertices and original row indices are retained. All six graphs are connected; no component was dropped or repaired. Original matrix values are preserved in the archived files, not treated as distances. The source names and per-file checksums are retained in each graph’s metadata.