# Graph-tool

**graph-tool** is a Python module for manipulation and statistical analysis of graphs (AKA networks). The core data structures and algorithms of graph-tool are implemented in C++, making extensive use of metaprogramming, based heavily on the Boost Graph Library. This type of approach can confer a level of performance which is comparable (both in memory usage and computation time) to that of a pure C++ library, which can be several orders of magnitude better than pure Python.[1]

Developer(s) | Tiago P. Peixoto |
---|---|

Stable release | 2.29
/ 12 July 2019 |

Repository | |

Written in | Python, C++ |

Operating system | OS X, Linux |

Type | Software library |

License | GPL |

Website | graph-tool |

Furthermore, many algorithms are implemented in parallel using OpenMP, which provides increased performance on multi-core architectures.

## Features

- Creation and manipulation of directed or undirected graphs.
- Association of arbitrary information to the vertices, edges or even the graph itself, by means of property maps.
- Filter vertices and/or edges "on the fly", such that they appear to have been removed.
- Support for dot, Graph Modelling Language and GraphML formats.
- Convenient and powerful graph drawing based on cairo or Graphviz.
- Support for typical statistical measurements: degree/property histogram, combined degree/property histogram, vertex-vertex correlations, assortativity, average vertex-vertex shortest path, etc.
- Support for several graph-theoretical algorithms: such as graph isomorphism, subgraph isomorphism, minimum spanning tree, connected components, dominator tree, maximum flow, etc.
- Support for several centrality measures.
- Support for clustering coefficients, as well as network motif statistics and community structure detection.
- Generation of random graphs, with arbitrary degree distribution and correlations.
- Support for well-established network models: Price, Barabási-Albert, Geometric Networks, Multidimensional lattice graph, etc.

## Suitability

Graph-tool can be used to work with very large graphs in a variety of contexts, including simulation of cellular tissue,[2] data mining,[3][4] analysis of social networks,[5][6] analysis of P2P systems,[7] large-scale modeling of agent-based systems,[8] study of academic Genealogy trees,[9] theoretical assessment and modeling of network clustering,[10] large-scale call graph analysis,[11] and analysis of the brain's Connectome.[12]

## References

- Graph-tool performance comparison, Graph-tool
- Bruno Monier et al, "Apico-basal forces exerted by apoptotic cells drive epithelium folding", Nature, 2015
- Ma, Shuai, et al. "Distributed graph pattern matching." Proceedings of the 21st international conference on World Wide Web. ACM, 2012.
- Ma, Shuai, et al. "Capturing topology in graph pattern matching." Proceedings of the VLDB Endowment 5.4 (2011): 310-321.
- Janssen, E., M. A. T. T. Hurshman, and N. A. U. Z. E. R. Kalyaniwalla. "Model selection for social networks using graphlets." Internet Mathematics (2012).
- Asadi, Hirad Cyrus. Design and implementation of a middleware for data analysis of social networks. Diss. M Sc thesis report, KTH School of Computer Science and Communication, Stockholm, Sweden, 2007.
- Teresniak, Sven, et al. "Information-Retrieval in einem P2P-Netz mit Small-World-Eigenschaften Simulation und Evaluation des SemPIR-Modells."
- Hamacher, Kay, and Stefan Katzenbeisser. "Public security: simulations need to replace conventional wisdom." Proceedings of the 2011 workshop on New security paradigms workshop. ACM, 2011.
- Miyahara, Edson Kiyohiro, Jesus P. Mena-Chalco, and Roberto M. Cesar-Jr. "Genealogia Acadêmica Lattes."
- Abdo, Alexandre H., and A. P. S. de Moura. "Clustering as a measure of the local topology of networks." arXiv preprint physics/0605235 (2006).
- Narayan, Ganesh, K. Gopinath, and V. Sridhar. "Structure and interpretation of computer programs." Theoretical Aspects of Software Engineering, 2008. TASE'08. 2nd IFIP/IEEE International Symposium on. IEEE, 2008.
- Gerhard, Stephan, et al. "The connectome viewer toolkit: an open source framework to manage, analyze, and visualize connectomes." Frontiers in neuroinformatics 5 (2011).