#

Note

This documents the development version of NetworkX. Documentation for the current release can be found here.

#

Graph types

NetworkX provides data structures and methods for storing graphs.

All NetworkX graph classes allow (hashable) Python objects as nodes and any Python object can be assigned as an edge attribute.

The choice of graph class depends on the structure of the graph you want to represent.

Which graph class should I use?

Networkx Class	Type	Self-loops allowed	Parallel edges allowed
Graph	undirected	Yes	No
DiGraph	directed	Yes	No
MultiGraph	undirected	Yes	Yes
MultiDiGraph	directed	Yes	Yes

Basic graph types

• Graph—Undirected graphs with self loops
  • Overview
  • Methods
• DiGraph—Directed graphs with self loops
  • Overview
  • Methods
• MultiGraph—Undirected graphs with self loops and parallel edges
  • Overview
  • Methods
• MultiDiGraph—Directed graphs with self loops and parallel edges
  • Overview
  • Methods
• Ordered Graphs—Consistently ordered graphs

Note

NetworkX uses dicts to store the nodes and neighbors in a graph. So the reporting of nodes and edges for the base graph classes will not necessarily be consistent across versions and platforms. If you need the order of nodes and edges to be consistent (e.g., when writing automated tests), please see OrderedGraph, OrderedDiGraph, OrderedMultiGraph, or OrderedMultiDiGraph, which behave like the base graph classes but give a consistent order for reporting of nodes and edges.

Graph Views

View of Graphs as SubGraph, Reverse, Directed, Undirected.

In some algorithms it is convenient to temporarily morph a graph to exclude some nodes or edges. It should be better to do that via a view than to remove and then re-add. In other algorithms it is convenient to temporarily morph a graph to reverse directed edges, or treat a directed graph as undirected, etc. This module provides those graph views.

The resulting views are essentially read-only graphs that report data from the orignal graph object. We provide an attribute G._graph which points to the underlying graph object.

Note: Since graphviews look like graphs, one can end up with view-of-view-of-view chains. Be careful with chains because they become very slow with about 15 nested views. For the common simple case of node induced subgraphs created from the graph class, we short-cut the chain by returning a subgraph of the original graph directly rather than a subgraph of a subgraph. We are careful not to disrupt any edge filter in the middle subgraph. In general, determining how to short-cut the chain is tricky and much harder with restricted_views than with induced subgraphs. Often it is easiest to use .copy() to avoid chains.

generic_graph_view(G[, create_using])
subgraph_view(G[, filter_node, filter_edge])	View of G applying a filter on nodes and edges.
reverse_view(G)	View of G with edge directions reversed

Filters

Note

Filters can be used with views to restrict the view (or expand it). They can filter nodes or filter edges. These examples are intended to help you build new ones. They may instead contain all the filters you ever need.

Filter factories to hide or show sets of nodes and edges.

These filters return the function used when creating SubGraph.

no_filter(*items)
hide_nodes(nodes)
hide_edges(edges)
hide_diedges(edges)
hide_multidiedges(edges)
hide_multiedges(edges)
show_nodes(nodes)
show_edges(edges)
show_diedges(edges)
show_multidiedges(edges)
show_multiedges(edges)