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INTRODUCTION


The NetworkX is python package which offers graph functionality and basic operations like graph creation, adding nodes, edged between two node, adding weight to edge, finding degree, in degree, out degree, Search, calculations and graph algorithms.


in short, NetworkX provides data structures for graphs (or networks) along with graph algorithms

The NetworkX package provides classes for graph objects, generators to create standard graphs, IO routines for reading in existing datasets, algorithms to analyze the resulting networks and some basic drawing tools. Most of the NetworkX API is provided by functions which take a graph object as an argument. Methods of the graph object are limited to basic manipulation and reporting.



NetworkX Basics


After starting Python, import the networkx module with (the recommended way)


import networkx as nx

To save repetition, in the documentation we assume that NetworkX has been imported this way. If importing networkx fails, it means that Python cannot find the installed module. Check your installation and your PYTHONPATH.


Install

Install the latest version of NetworkX:

$ pip install networkx


The following basic graph types are provided as Python classes:


Graph: This class implements an undirected graph. It ignores multiple edges between two nodes. It does allow self-loop edges between a node and itself.


DiGraph: Directed graphs, that is, graphs with directed edges. Provides operations common to directed graphs, (a subclass of Graph).


MultiGraph: A flexible graph class that allows multiple undirected edges between pairs of nodes. The additional flexibility leads to some degradation in performance, though usually not significant.


MultiDiGraph: A directed version of a MultiGraph.


Empty graph-like objects are created with


G = nx.Graph()
G = nx.DiGraph()
G = nx.MultiGraph()
G = nx.MultiDiGraph()

  • All graph classes allow any hashable object as a node.

  • Hashable objects include strings, tuples, integers, and more.

  • Arbitrary edge attributes such as weights and labels can be associated with an edge.

  • The graph internal data structures are based on an adjacency list representation and implemented using Python dictionary data structures.

  • The graph adjacency structure is implemented as a Python dictionary of dictionaries; the outer dictionary is keyed by nodes to values that are themselves dictionaries keyed by neighboring node to the edge attributes associated with that edge.

  • This "dict-of-dicts” structure allows fast addition, deletion, and lookup of nodes and neighbors in large graphs. The underlying data structure is accessed directly by methods (the programming interface “API”) in the class definitions.

  • All functions, on the other hand, manipulate graph-like objects solely via those API methods and not by acting directly on the data structure.

This design allows for possible replacement of the ‘dicts-of-dicts’-based data structure with an alternative data structure that implements the same methods.



Graphs


The basic graph classes are named: Graph, DiGraph, MultiGraph, and MultiDiGraph


After creating object of NetworkX class the first choice to be made is what type of graph object to use.

A graph (network) is a collection of nodes together with a collection of edges that are pairs of nodes. Attributes are often associated with nodes and/or edges.

NetworkX graph objects come in different flavors depending on two main properties of the network:

  • Directed: Are the edges directed? Does the order of the edge pairs (𝑢, 𝑣) matter? A directed graph is specified by the “Di” prefix in the class name, e.g. DiGraph(). We make this distinction because many classical graph properties are defined differently for directed graphs.

  • Multi-edges: Are multiple edges allowed between each pair of nodes? As you might imagine, multiple edges requires a different data structure, though clever users could design edge data attributes to support this functionality. We provide a standard data structure and interface for this type of graph using the prefix “Multi”, e.g., MultiGraph().

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