We provide a standard data structureĪnd interface for this type of graph using the prefix “Multi”, Structure, though clever users could design edge data attributes to Multi-edges: Are multiple edges allowed between each pair of nodes?Īs you might imagine, multiple edges requires a different data We make this distinctionīecause many classical graph properties are defined differently for Pairs \((u, v)\) matter? A directed graph is specified by the “Di” NetworkX graph objects come inĭifferent flavors depending on two main properties of the network:ĭirected: Are the edges directed? Does the order of the edge Often associated with nodes and/or edges. With a collection of edges that are pairs of nodes. A graph (network) is a collection of nodes together The first choice to be made when using NetworkX is what type of graph This design allows for possible replacement of the ‘dicts-of-dicts’-basedĭatastructure with an alternative datastructure that implements the Solely via those API methods and not by acting directly on the datastructure. The underlying datastructure is accessed directlyīy methods (the programming interface “API”) in the class definitions.Īll functions, on the other hand, manipulate graph-like objects This “dict-of-dicts” structureĪllows fast addition, deletion, and lookup of nodes and neighbors in Themselves dictionaries keyed by neighboring node to theĮdge attributes associated with that edge. The graph internal data structures are based on anĪdjacency list representation and implemented usingĭictionaries the outer dictionary is keyed by nodes to values that are Hashable objects include strings, tuples, integers, and more.Īrbitrary edge attributes such as weights and labels MultiDiGraph ()Īll graph classes allow any hashable object as a node. NetworkX Basics #Īfter starting Python, import the networkx module with (the recommended way) If you have suggestions or questions please contact us by joining theĬlasses are named using CamelCase (capital letters at the start of each word).įunctions, methods and variable names are lower_case_underscore (lowercase withĪn underscore representing a space between words).
Download nx 10 documentation code#
This Python code is actually a good way to learn more about network algorithms,īut we have put a lot of effort into making the documentation sufficient and friendly. The source code for each module is meant to be easy to read and reading
It also makes it easier for newcomers to learn about the package in stages. This provides modularity of code and documentation. Methods of the graph object are limited to basic manipulationĪnd reporting. Most of the NetworkX API is provided by functions which take a graph objectĪs an argument. The resulting networks and some basic drawing tools. Graphs, IO routines for reading in existing datasets, algorithms to analyze The package provides classes for graph objects, generators to create standard The structure of NetworkX can be seen by the organization of its source code.
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