NetworkX vs graph-tool
Struggling to choose between NetworkX and graph-tool? Both products offer unique advantages, making it a tough decision.
NetworkX is a Development solution with tags like graph-theory, network-analysis, data-structures.
It boasts features such as Graph and network data structures, Algorithms for network analysis, Tools for generating synthetic networks, Built-in graph drawing functionality, Integration with NumPy, SciPy, and Pandas and pros including Open source and free to use, Large user community, Wide range of algorithms and analytics, Flexible data structures, Easy to learn and use.
On the other hand, graph-tool is a Development product tagged with graphs, networks, analysis, statistics, python.
Its standout features include 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, and it shines with pros like Efficient implementation in C++, Integration with Python for ease of use, Support for large networks, Active development and maintenance.
To help you make an informed decision, we've compiled a comprehensive comparison of these two products, delving into their features, pros, cons, pricing, and more. Get ready to explore the nuances that set them apart and determine which one is the perfect fit for your requirements.
NetworkX
NetworkX is an open-source Python package for creating, manipulating, and studying the structure, dynamics, and functions of complex networks. It provides tools for analyzing node and edge attributes, generating synthetic networks, calculating network measures, drawing networks, and more.
Categories:
NetworkX Features
- Graph and network data structures
- Algorithms for network analysis
- Tools for generating synthetic networks
- Built-in graph drawing functionality
- Integration with NumPy, SciPy, and Pandas
Pricing
- Open Source
Pros
Cons
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.
Categories:
Graph-tool 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