Graph-tool
graph-tool is an efficient Python module for manipulation and statistical analysis of graphs and networks. It provides a comprehensive set of data structures and algorithms for networks based on statistical physics and complex networks theory.
graph-tool: Efficient Python Module for Network Manipulation and Analysis
graph-tool is an efficient Python module for manipulation and statistical analysis of graphs and networks. It provides a comprehensive set of data structures and algorithms for networks based on statistical physics and complex networks theory.
What is Graph-tool?
graph-tool is an efficient Python module for manipulation and statistical analysis of large graphs and networks. It is designed to be both versatile and high-performance, aimed towards complex network research and analysis of large datasets.
Some key features of graph-tool include:
- Implementation of data structures and algorithms for analysis of large networks based on statistical physics and complex networks theory
- Flexible, versatile and modular architecture allowing easy development of new algorithms and structures
- C++ backend combined with Python language bindings for easy development and execution while retaining computational efficiency
- Support for stochastic graph generation models commonly used in physics, biology, social science and more
- Statistics for vertices, edges, motifs, clusters, centrality measures and more
- Filters, flow algorithms, BSSN betweenness and other common network analysis procedures
- Built-in visualization and interface to external graph drawing software
- Parallel processing capabilities for multicore machines and clusters
graph-tool can handle large networks with hundreds of thousands or even millions of vertices efficiently. It enables researchers across fields like physics, biology, social science, neuroscience and more to analyze complex real-world networks.
Graph-tool Features
Features
- Graph and network objects
- Algorithms for structural analysis
- Generators for various random graph models
- I/O for various graph formats
- Statistical inference algorithms
- Visualization tools
Pricing
- Open Source
Pros
Efficient implementation in C++
Integration with Python for ease of use
Support for large networks
Active development and maintenance
Cons
Steep learning curve
Limited documentation and examples
Not very beginner friendly
Official Links
Reviews & Ratings
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