Cara menggunakan undirected graph python

Nodes

The graph

>>> H = nx.path_graph(10)
>>> G.add_nodes_from(H)

>>> G.add_node(1)

or add nodes from any container, such as a list

>>> G.add_nodes_from([2, 3])

You can also add nodes along with node attributes if your container yields 2-tuples of the form

>>> H = nx.path_graph(10)
>>> G.add_nodes_from(H)

>>> G.add_nodes_from([
...     (4, {"color": "red"}),
...     (5, {"color": "green"}),
... ])

Node attributes are discussed further .

Nodes from one graph can be incorporated into another:

>>> H = nx.path_graph(10)
>>> G.add_nodes_from(H)

>>> H = nx.path_graph(10)
>>> G.add_nodes_from(H)

>>> H = nx.path_graph(10)
>>> G.add_nodes_from(H)

>>> H = nx.path_graph(10)
>>> G.add_nodes_from(H)

>>> H = nx.path_graph(10)
>>> G.add_nodes_from(H)

>>> H = nx.path_graph(10)
>>> G.add_nodes_from(H)

>>> G.add_node(H)

The graph

>>> H = nx.path_graph(10)
>>> G.add_nodes_from(H)

>>> H = nx.path_graph(10)
>>> G.add_nodes_from(H)

>>> H = nx.path_graph(10)
>>> G.add_nodes_from(H)

Note

You should not change the node object if the hash depends on its contents.

Edges

>>> H = nx.path_graph(10)
>>> G.add_nodes_from(H)

>>> G.add_edge(1, 2)
>>> e = (2, 3)
>>> G.add_edge(*e)  # unpack edge tuple*

by adding a list of edges,

>>> G.add_edges_from([(1, 2), (1, 3)])

or by adding any of edges. An ebunch is any iterable container of edge-tuples. An edge-tuple can be a 2-tuple of nodes or a 3-tuple with 2 nodes followed by an edge attribute dictionary, e.g.,

>>> G.add_node(H)

>>> G.add_edges_from(H.edges)

There are no complaints when adding existing nodes or edges. For example, after removing all nodes and edges,

>>> G.clear()

we add new nodes/edges and NetworkX quietly ignores any that are already present.

>>> G.add_node(1)

At this stage the graph

>>> H = nx.path_graph(10)
>>> G.add_nodes_from(H)

>>> G.add_node(1)

Note

The order of adjacency reporting (e.g., , , ) is the order of edge addition. However, the order of G.edges is the order of the adjacencies which includes both the order of the nodes and each node’s adjacencies. See example below:

>>> G.add_node(1)

Examining elements of a graph

We can examine the nodes and edges. Four basic graph properties facilitate reporting:

>>> G.add_node(H)

>>> G.add_node(H)

>>> G.add_node(H)

>>> G.add_edge(1, 2)
>>> e = (2, 3)
>>> G.add_edge(*e)  # unpack edge tuple*

>>> G.add_edge(1, 2)
>>> e = (2, 3)
>>> G.add_edge(*e)  # unpack edge tuple*

>>> G.add_edge(1, 2)
>>> e = (2, 3)
>>> G.add_edge(*e)  # unpack edge tuple*

>>> G.add_node(1)

One can specify to report the edges and degree from a subset of all nodes using an . An nbunch is any of:

>>> H = nx.path_graph(10)
>>> G.add_nodes_from(H)

>>> G.add_node(1)

Removing elements from a graph

One can remove nodes and edges from the graph in a similar fashion to adding. Use methods , , and , e.g.

>>> G.add_node(1)

Using the graph constructors

Graph objects do not have to be built up incrementally - data specifying graph structure can be passed directly to the constructors of the various graph classes. When creating a graph structure by instantiating one of the graph classes you can specify data in several formats.

>>> G.add_node(1)

What to use as nodes and edges

You might notice that nodes and edges are not specified as NetworkX objects. This leaves you free to use meaningful items as nodes and edges. The most common choices are numbers or strings, but a node can be any hashable object (except

>>> H = nx.path_graph(10)
>>> G.add_nodes_from(H)

>>> G.add_edges_from([(1, 2), (1, 3)])

>>> G.add_edges_from([(1, 2), (1, 3)])

As an example,

>>> G.add_edges_from([(1, 2), (1, 3)])

>>> G.add_edges_from([(1, 2), (1, 3)])

>>> G.add_edges_from([(1, 2), (1, 3)])

We have found this power quite useful, but its abuse can lead to surprising behavior unless one is familiar with Python. If in doubt, consider using to obtain a more traditional graph with integer labels.

Accessing edges and neighbors

In addition to the views , and , access to edges and neighbors is possible using subscript notation.

>>> G.add_node(1)

You can get/set the attributes of an edge using subscript notation if the edge already exists.

>>> G.add_node(1)

Fast examination of all (node, adjacency) pairs is achieved using

>>> G.add_edges_from([(1, 2), (1, 3)])

>>> G.add_edges_from([(1, 2), (1, 3)])

>>> G.add_node(1)

Convenient access to all edges is achieved with the edges property.

>>> G.add_nodes_from([2, 3])

Adding attributes to graphs, nodes, and edges

Attributes such as weights, labels, colors, or whatever Python object you like, can be attached to graphs, nodes, or edges.

Each graph, node, and edge can hold key/value attribute pairs in an associated attribute dictionary (the keys must be hashable). By default these are empty, but attributes can be added or changed using

>>> G.add_edges_from(H.edges)

>>> G.add_edges_from(H.edges)

>>> G.add_edges_from(H.edges)

>>> G.add_node(H)

>>> G.add_node(H)

>>> H = nx.path_graph(10)
>>> G.add_nodes_from(H)

>>> G.add_nodes_from([2, 3])

>>> G.add_nodes_from([2, 3])

>>> G.add_edges_from(H.edges)

>>> G.add_edges_from(H.edges)

>>> G.add_node(H)

>>> G.add_nodes_from([2, 3])

>>> G.add_node(H)

>>> G.clear()

>>> G.clear()

>>> G.clear()

>>> G.add_nodes_from([2, 3])

>>> G.clear()

Directed graphs

The class provides additional methods and properties specific to directed edges, e.g., , , , etc. To allow algorithms to work with both classes easily, the directed versions of is equivalent to while reports the sum of and even though that may feel inconsistent at times.

>>> G.add_nodes_from([2, 3])

Some algorithms work only for directed graphs and others are not well defined for directed graphs. Indeed the tendency to lump directed and undirected graphs together is dangerous. If you want to treat a directed graph as undirected for some measurement you should probably convert it using or with

>>> G.add_nodes_from([2, 3])

Multigraphs

NetworkX provides classes for graphs which allow multiple edges between any pair of nodes. The and classes allow you to add the same edge twice, possibly with different edge data. This can be powerful for some applications, but many algorithms are not well defined on such graphs. Where results are well defined, e.g., we provide the function. Otherwise you should convert to a standard graph in a way that makes the measurement well defined.

>>> G.add_nodes_from([2, 3])

Graph generators and graph operations

In addition to constructing graphs node-by-node or edge-by-edge, they can also be generated by

>>> G.add_node(1)

>>> G.add_node(1)

>>> G.add_node(1)

>>> G.add_node(1)

>>> G.add_node(1)

>>> G.add_node(1)

>>> G.add_nodes_from([2, 3])

>>> G.add_nodes_from([2, 3])

>>> G.add_nodes_from([
...     (4, {"color": "red"}),
...     (5, {"color": "green"}),
... ])

Analyzing graphs

The structure of

>>> H = nx.path_graph(10)
>>> G.add_nodes_from(H)

>>> G.add_nodes_from([
...     (4, {"color": "red"}),
...     (5, {"color": "green"}),
... ])

Some functions with large output iterate over (node, value) 2-tuples. These are easily stored in a structure if you desire.

>>> G.add_nodes_from([
...     (4, {"color": "red"}),
...     (5, {"color": "green"}),
... ])

See Algorithms for details on graph algorithms supported.

Drawing graphs

NetworkX is not primarily a graph drawing package but basic drawing with Matplotlib as well as an interface to use the open source Graphviz software package are included. These are part of the networkx.drawing module and will be imported if possible.

First import Matplotlib’s plot interface (pylab works too)

>>> G.add_nodes_from([
...     (4, {"color": "red"}),
...     (5, {"color": "green"}),
... ])

To test if the import of was successful draw

>>> H = nx.path_graph(10)
>>> G.add_nodes_from(H)

>>> G.add_nodes_from([
...     (4, {"color": "red"}),
...     (5, {"color": "green"}),
... ])

(png, hires.png, pdf)

when drawing to an interactive display. Note that you may need to issue a Matplotlib

>>> G.add_nodes_from([
...     (4, {"color": "red"}),
...     (5, {"color": "green"}),
... ])

command if you are not using matplotlib in interactive mode.

>>> G.add_nodes_from([
...     (4, {"color": "red"}),
...     (5, {"color": "green"}),
... ])

(png, hires.png, pdf)

You can find additional options via and layouts via the . You can use multiple shells with .

>>> G.add_nodes_from([
...     (4, {"color": "red"}),
...     (5, {"color": "green"}),
... ])

(png, hires.png, pdf)

To save drawings to a file, use, for example

>>> G.add_nodes_from([
...     (4, {"color": "red"}),
...     (5, {"color": "green"}),
... ])

This function writes to the file

>>> G.add_node(1)