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.

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  1. Sun, Peng Gang; Che, Wanping; Quan, Yining; Wang, Shuzhen; Miao, Qiguang: Random networks are heterogeneous exhibiting a multi-scaling law (2022)
  2. Alozie, Glory Uche; Arulselvan, Ashwin; Akartunalı, Kerem; Pasiliao, Eduardo L. jun.: Efficient methods for the distance-based critical node detection problem in complex networks (2021)
  3. Gaucher, Solenne; Klopp, Olga; Robin, Geneviève: Outlier detection in networks with missing links (2021)
  4. Li, Shudong; Jiang, Laiyuan; Wu, Xiaobo; Han, Weihong; Zhao, Dawei; Wang, Zhen: A weighted network community detection algorithm based on deep learning (2021)
  5. Mattenet, Alex; Davidson, Ian; Nijssen, Siegfried; Schaus, Pierre: Generic constraint-based block modeling using constraint programming (2021)
  6. Wang, Dong; Zhao, Yi; Luo, Jianfeng; Leng, Hui: Simplicial SIRS epidemic models with nonlinear incidence rates (2021)
  7. Yuan, Quan; Liu, Binghui: Community detection via an efficient nonconvex optimization approach based on modularity (2021)
  8. Jabbour, Said; Mhadhbi, Nizar; Raddaoui, Badran; Sais, Lakhdar: SAT-based models for overlapping community detection in networks (2020)
  9. Demaine, Erik D.; Reidl, Felix; Rossmanith, Peter; F. S. Sánchez Villaamil, Fernando; Sikdar, Somnath; Sullivan, Blair D.: Structural sparsity of complex networks: bounded expansion in random models and real-world graphs (2019)
  10. Hu, Fang; Zhu, Yanhui; Liu, Jia; Jia, Yalin: Computing communities in complex networks using the Dirichlet processing Gaussian mixture model with spectral clustering (2019)
  11. Weng, Tongfeng; Yang, Huijie; Gu, Changgui; Zhang, Jie; Hui, Pan; Small, Michael: Predator-prey games on complex networks (2019)
  12. Chalupa, David: An order-based algorithm for minimum dominating set with application in graph mining (2018)
  13. Ibnoulouafi, Ahmed; El Haziti, Mohamed: Density centrality: identifying influential nodes based on area density formula (2018)
  14. Ibnoulouafi, Ahmed; El Haziti, Mohamed; Cherifi, Hocine: M-centrality: identifying key nodes based on global position and local degree variation (2018)
  15. Knueven, Ben; Ostrowski, Jim; Pokutta, Sebastian: Detecting almost symmetries of graphs (2018)
  16. Li, Zichao; Mucha, Peter J.; Taylor, Dane: Network-ensemble comparisons with stochastic rewiring and von Neumann entropy (2018)
  17. Micale, Giovanni; Giugno, Rosalba; Ferro, Alfredo; Mongiovì, Misael; Shasha, Dennis; Pulvirenti, Alfredo: Fast analytical methods for finding significant labeled graph motifs (2018)
  18. Nadara, Wojciech; Pilipczuk, Marcin; Rabinovich, Roman; Reidl, Felix; Siebertz, Sebastian: Empirical evaluation of approximation algorithms for generalized graph coloring and uniform quasi-wideness (2018)
  19. 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)
  20. Zhu, Zhi-Qiang: A novel method of generating tunable network topologies for social simulation (2018)

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