ⓘ graph-tool is a Python module for manipulation and statistical analysis of graphs. The core data structures and algorithms of graph-tool are implemented in C++, ..


ⓘ Graph-tool

graph-tool is a Python module for manipulation and statistical analysis of graphs. 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 to that of a pure C++ library, which can be several orders of magnitude better than pure Python.

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


1. Features

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

2. Suitability

Graph-tool can be used to work with very large graphs in a variety of contexts, including simulation of cellular tissue, data mining, analysis of social networks, analysis of P2P systems, large-scale modeling of agent-based systems, study of academic Genealogy trees, theoretical assessment and modeling of network clustering, large-scale call graph analysis, and analysis of the brains Connectome.

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