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 103 articles )

Showing results 1 to 20 of 103.
Sorted by year (citations)

1 2 3 4 5 6 next

  1. Knueven, Ben; Ostrowski, Jim; Pokutta, Sebastian: Detecting almost symmetries of graphs (2018)
  2. Li, Zichao; Mucha, Peter J.; Taylor, Dane: Network-ensemble comparisons with stochastic rewiring and von Neumann entropy (2018)
  3. Staritsyn, Maksim Vladimirovich; Maltugueva, Nadezhda Stanislavovna; Pogodaev, Nikolaĭ Il’ich; Sorokin, Stepan Pavlovich: Impulsive control of systems with network structure describing spread of political influence (2018)
  4. Avrachenkov, K.; Chebotarev, P.; Mishenin, A.: Semi-supervised learning with regularized Laplacian (2017)
  5. Beiranvand, Vahid; Hare, Warren; Lucet, Yves: Best practices for comparing optimization algorithms (2017)
  6. Bian, Tian; Deng, Yong: A new evidential methodology of identifying influential nodes in complex networks (2017)
  7. Meghanathan, Natarajan: Retracted article: A distance vector similarity metric for complex networks (2017)
  8. Sun, Peng Gang; Ma, Xiaoke; Chi, Juan: Dominating complex networks by identifying minimum skeletons (2017)
  9. Yang, Jin-Xuan; Zhang, Xiao-Dong: A spectral method to detect community structure based on distance modularity matrix (2017)
  10. Žalik, Krista Rizman: Community detection in networks using new update rules for label propagation (2017)
  11. Meghanathan, Natarajan: A greedy algorithm for neighborhood overlap-based community detection (2016)
  12. Wellin, Paul: Essentials of programming in Mathematica (2016)
  13. Eltayeb, Hassan (ed.); Kilicman, Adem (ed.); Jleli, Mohamed Boussairi (ed.): Fractional integral transform and application (2015)
  14. Olteanu, Madalina; Villa-Vialaneix, Nathalie: Using SOMbrero for clustering and visualizing graphs (2015)
  15. Qian, Liqiang; Bu, Zhan; Lu, Mei; Cao, Jie; Wu, Zhiang: Extracting backbones from weighted complex networks with incomplete information (2015)
  16. Sun, Peng Gang: Controllability and modularity of complex networks (2015)
  17. Tim Benham, Qibin Duan, Dirk P. Kroese, Benoit Liquet: CEoptim: Cross-Entropy R Package for Optimization (2015) arXiv
  18. Cafieri, Sonia; Costa, Alberto; Hansen, Pierre: Reformulation of a model for hierarchical divisive graph modularity maximization (2014)
  19. Cafieri, Sonia; Hansen, Pierre; Liberti, Leo: Improving heuristics for network modularity maximization using an exact algorithm (2014)
  20. Hu, Shengze; Wang, Zhenwen: Detecting communities in networks using a Bayesian nonparametric method (2014)

1 2 3 4 5 6 next