The Boost Graph Library (BGL). Graphs are mathematical abstractions that are useful for solving many types of problems in computer science. Consequently, these abstractions must also be represented in computer programs. A standardized generic interface for traversing graphs is of utmost importance to encourage reuse of graph algorithms and data structures. Part of the Boost Graph Library is a generic interface that allows access to a graph’s structure, but hides the details of the implementation. This is an “open” interface in the sense that any graph library that implements this interface will be interoperable with the BGL generic algorithms and with other algorithms that also use this interface. The BGL provides some general purpose graph classes that conform to this interface, but they are not meant to be the “only” graph classes; there certainly will be other graph classes that are better for certain situations. We believe that the main contribution of the The BGL is the formulation of this interface. The BGL graph interface and graph components are generic, in the same sense as the Standard Template Library (STL) [2]. In the following sections, we review the role that generic programming plays in the STL and compare that to how we applied generic programming in the context of graphs.

References in zbMATH (referenced in 61 articles )

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  1. Cicerone, Serafino; D’Emidio, Mattia; Di Stefano, Gabriele; Navarra, Alfredo: On the effectiveness of the genetic paradigm for polygonization (2021)
  2. Michail, Dimitrios; Kinable, Joris; Naveh, Barak; Sichi, John V.: JGraphT -- a Java library for graph data structures and algorithms (2020)
  3. Vilas, Fernando E.; Olinick, Eli V.; Matula, David W.: Bounds on maximum concurrent flow in random bipartite graphs (2020)
  4. Ji, Zhe; Fu, Lin; Hu, Xiangyu Y.; Adams, Nikolaus A.: A new multi-resolution parallel framework for SPH (2019)
  5. Koster, A. M. C. A.; Scheidweiler, R.; Tieves, M.: A flow based pruning scheme for enumerative equitable coloring algorithms (2019)
  6. Møyner, Olav; Nilsen, Halvor Møll: Multiresolution coupled vertical equilibrium model for fast flexible simulation of CO(_2) storage (2019)
  7. Ramani, Arjun S.; Eikmeier, Nicole; Gleich, David F.: Coin-flipping, Ball-dropping, and Grass-hopping for generating random graphs from matrices of edge probabilities (2019)
  8. Toth, Csaba D. (ed.); Goodman, Jacob E. (ed.); O’Rourke, Joseph (ed.): Handbook of discrete and computational geometry (2017)
  9. Vömel, Christof; de Lorenzi, Flavio; Beer, Samuel; Fuchs, Erwin: The secret life of keys: on the calculation of mechanical lock systems (2017)
  10. Xu, Hong; Satish Kumar, T. K.; Koenig, Sven: The Nemhauser-Trotter reduction and lifted message passing for the weighted CSP (2017)
  11. Akhmedov, Murodzhon; Kwee, Ivo; Montemanni, Roberto: A divide and conquer matheuristic algorithm for the prize-collecting Steiner tree problem (2016)
  12. Andersen, Jakob L.; Flamm, Christoph; Merkle, Daniel; Stadler, Peter F.: A software package for chemically inspired graph transformation (2016)
  13. Caravelli, Francesco; Bardoscia, Marco; Caccioli, Fabio: Emergence of giant strongly connected components in continuum disk-spin percolation (2016)
  14. Schuppan, Viktor: Enhancing unsatisfiable cores for LTL with information on temporal relevance (2016)
  15. Schuppan, Viktor: Extracting unsatisfiable cores for LTL via temporal resolution (2016)
  16. Burger, Alewyn Petrus; de Villiers, Anton Pierre; van Vuuren, Jan Harm: Edge stability in secure graph domination (2015)
  17. Filar, Jerzy A.; Haythorpe, Michael; Rossomakhine, Serguei: A new heuristic for detecting non-Hamiltonicity in cubic graphs (2015)
  18. Keiren, Jeroen J. A.: Benchmarks for parity games (2015) ioport
  19. Kovács, Péter: Minimum-cost flow algorithms: an experimental evaluation (2015)
  20. Wardhana, Nicholas Mario; Johan, Henry; Seah, Hock Soon: Subregion graph: a path planning acceleration structure for characters with various motion types in very large environments (2015)

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