graph-tool is a Python module for manipulation and statistical analysis of graphs[disambiguation needed] (a.k.a. networks).

graph-tool是一个操作和统计分析图表的Python模块。

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.

graph-tool核心的数据结构和算法是用c++实现的,其大量使用元编程,依赖于Boost Graph库。

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]

这种方法会带来一定程度的性能优势，甚至能与纯c++库相匹敌(在内存使用和计算时间上),这可以比单纯使用Python好几个数量级。

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

此外,许多算法由并行使用OpenMP实现,这使得其在多核体系结构机器上的性能有显著提升。

内容

特征
适用性
参考文献
外部链接

1.特征

Creation and manipulation of directed or undirected graphs.

直接或间接地创建和操纵图表。

Association of arbitrary information to the vertices, edges or even the graph itself, by means of property maps.

通过属性映射，将任意的信息与顶点、边甚至是图本身相关联起来。

Filter vertices and/or edges "on the fly", such that they appear to have been removed.

“动态”地过滤顶点和/或边,这样它们看起来似乎已经被移除。

Support for dot, Graph Modelling Language and GraphML formats.

支持点,图形建模语言和GraphML格式。

Convenient and powerful graph drawing based on cairo or Graphviz.

便捷和强大的图形绘制能力（基于cairo和Graphviz）。

Support for typical statistical measurements: degree/property histogram, combined degree/property histogram, vertex-vertex correlations, assortativity, average vertex-vertex shortest path, etc.

支持典型的统计指标:degree/property直方图,combined degree/property直方图,vertex-vertex相关性,assortativity vertex-vertex平均最短路径等。

Support for several graph-theoretical algorithms: such as graph isomorphism, subgraph isomormism, minimum spanning tree, connected components, dominator tree, maximum flow, etc.

支持几个图形理论算法:例如，图同构，子图同构，最小生成树，连接组件，支配树，最大流法。

Support for several centrality measures.

支持多种中心性。

Support for clustering coefficients, as well as network motif statistics and community structure detection.

支持集群系数,以及网络motif统计和群体结构检测。

Generation of random graphs, with arbitrary degree distribution and correlations.

具有任意分布度和相关性的随机图的生成,。

Support for well-established network models: Price, Barabási-Albert, Geometric Networks, Multidimensional lattice graph, etc.

支持已经建立的网络模型：如，Price、Barabasi-Albert、几何网络多维网格图等。

2.适用性

Graph-tool can be used to work with very large graphs in a variety of contexts, including
Graph-tool可以用来处理各种情况下非常大的图,包括

simulation of cellular tissue
模拟细胞组织
data mining
数据挖掘
analysis of social networks
社交网络分析
analysis of P2P systems,[7]
P2P系统分析
large-scale modeling of agent-based systems,[8]
大规模的基于主体系统的建模
study of academic Genealogy trees,[9]
研究学术家谱树
theoretical assessment
理论评估
and modeling of network clustering,[10]
网络聚类和建模
large-scale call graph analysis,[11][12]
大规模的调用图分析
and analysis of the brain's Connectome.[13]
分析大脑的连接体

3.参考文献

参见原链接

4.外部链接

Graph-tool官网

来源网址：Graph-tool - From Wikipedia, the free encyclopedia

巴特西

Graph-tool简介 - wiki

1.特征

2.适用性

3.参考文献

4.外部链接

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