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.^[1] Many algorithms are implemented in parallel using OpenMP, which provides increased performance on multi-core architectures.

Graph Tool

Developer(s)	Tiago P. Peixoto
Stable release	2.45 / 22 May 2022; 2 years ago (2022-05-22)
Repository	git.skewed.de/count0/graph-tool
Written in	Python, C++
Operating system	OS X, Linux
Type	Software library
License	LGPL
Website	graph-tool.skewed.de

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 ^{[clarification needed]} 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]