GraphBase

The Stanford GraphBase is a freely available collection of computer programs and data useful for testing and comparing combinatorial algorithms. The programs generate a large number of graphs with a great variety of properties. Some of the graphs are based on data from the “real world”: Five-letter words of English, the characters in classical works of fiction, highway distances between cities, input-output statistics of the US economy, college football scores, computational logic circuits, the Mona Lisa, etc. Others are based on regular mathematical constructions such as lattices and quaternions. Graphs can be modified and combined by union, intersection, complementation, product, and forming line graphs. A general induced-graph routine allows omission and/or collapsing and/or splitting of vertices, and/or replacement of vertices by arbitrary graphs. Each graph has an identifying name, so that researchers all over the world can compare results on identical graphs and so that experiments are reproducible. For example, graphs such as book (“homer”, 280, 0, 1, 0, 0, 1, 1, 0) and random_bigraph (128, 128, 1000, -1, 0, 0, 0, 0, 314159) and all-perms (9) are well defined. Conclusion: This paper is a brief overview of the system. Complete details appeared in the author’s book with the same title, published by ACM Press in 1993.


References in zbMATH (referenced in 83 articles )

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  1. Avrachenkov, K.; Chebotarev, P.; Mishenin, A.: Semi-supervised learning with regularized Laplacian (2017)
  2. Wellin, Paul: Essentials of programming in Mathematica (2016)
  3. Olteanu, Madalina; Villa-Vialaneix, Nathalie: Using SOMbrero for clustering and visualizing graphs (2015)
  4. Qian, Liqiang; Bu, Zhan; Lu, Mei; Cao, Jie; Wu, Zhiang: Extracting backbones from weighted complex networks with incomplete information (2015)
  5. Tim Benham, Qibin Duan, Dirk P. Kroese, Benoit Liquet: CEoptim: Cross-Entropy R Package for Optimization (2015) arXiv
  6. Cafieri, Sonia; Costa, Alberto; Hansen, Pierre: Reformulation of a model for hierarchical divisive graph modularity maximization (2014)
  7. Cafieri, Sonia; Hansen, Pierre; Liberti, Leo: Improving heuristics for network modularity maximization using an exact algorithm (2014)
  8. Aouchiche, Mustapha; Caporossi, Gilles; Hansen, Pierre: Open problems on graph eigenvalues studied with AutoGraphiX (2013)
  9. E, Weinan; Lu, Jianfeng; Yao, Yuan: The landscape of complex networks -- critical nodes and a hierarchical decomposition (2013)
  10. Gronemann, Martin; Jünger, Michael: Drawing clustered graphs as topographic maps (2013)
  11. Hungerländer, P.; Rendl, F.: Semidefinite relaxations of ordering problems (2013)
  12. Silva, Thiago Christiano; Zhao, Liang: Uncovering overlapping cluster structures via stochastic competitive learning (2013) ioport
  13. Bachmaier, Christian; Brandenburg, Franz Josef; Effinger, Philip; Gutwenger, Carsten; Katajainen, Jyrki; Klein, Karsten; Spönemann, Miro; Stegmaier, Matthias; Wybrow, Michael: The Open Graph Archive: a community-driven effort (2012)
  14. Liers, F.; Pardella, G.: Partitioning planar graphs: a fast combinatorial approach for max-cut (2012)
  15. Martí, Rafael; Reinelt, Gerhard; Duarte, Abraham: A benchmark library and a comparison of heuristic methods for the linear ordering problem (2012)
  16. Shi, Quan; Xiao, Yanghua; Bessis, Nik; Lu, Yiqi; Chen, Yaoliang; Hill, Richard: Optimizing $K^2$ trees: a case for validating the maturity of network of practices (2012)
  17. Cambazard, Hadrien; Horan, John; O’Mahony, Eoin; O’Sullivan, Barry: Domino portrait generation: a fast and scalable approach (2011)
  18. Griechisch, Erika; Pluhár, András: Community detection by using the extended modularity (2011)
  19. Kuo, Ching-Chung: Optimal assignment of resources to strengthen the weakest link in an uncertain environment (2011)
  20. Martí, Rafael; Reinelt, Gerhard: The linear ordering problem. Exact and heuristic methods in combinatorial optimization. (2011)

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