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Graph-tool, a free Python module for manipulation and statistical analysis of graphs. NetworkX, an open source Python library for studying complex graphs. Tulip (software) is a free software in the domain of information visualisation capable of manipulating huge graphs (with more than 1.000.000 elements).
NetworkX has applications in any field that studies data as graphs or networks, such as mathematics, physics, biology, computer science and social science. [20] The nodes in a NetworkX graph can be specialized to hold any data, and the data stored in edges is arbitrary, further making it widely applicable to different fields.
GraphML is an XML-based file format for graphs. The GraphML file format results from the joint effort of the graph drawing community to define a common format for exchanging graph structure data. It uses an XML-based syntax and supports the entire range of possible graph structure constellations including directed, undirected, mixed graphs ...
JSON is a popular format for exchanging object data between systems. Frequently there's a need for a stream of objects to be sent over a single connection, such as a stock ticker or application log records. [1]
Networkx is a Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks; Graph-tool is a python module for efficient analysis of graphs. Its core data structures and algorithms are implemented in C++, with heavy use of Template metaprogramming, based on the Boost Graph Library.
Existing Eiffel software uses the string classes (such as STRING_8) from the Eiffel libraries, but Eiffel software written for .NET must use the .NET string class (System.String) in many cases, for example when calling .NET methods which expect items of the .NET type to be passed as arguments. So, the conversion of these types back and forth ...
XGMML is an XML 1.0-based markup language based on the Graph Modeling Language.The language uses tags to describe the edges and notes on a graph. It is primarily used to make the graphs more easily exchangeable and readable by different graphing software.
Ties based on co-occurrence can then be used to construct semantic networks. This process includes identifying keywords in the text, constructing co-occurrence networks, and analyzing the networks to find central words and clusters of themes in the network. It is a particularly useful method to analyze large text and big data. [40]