Realization of drawing complex network diagram with python networkx packet

1. Create a diagram

import networkx as nx
g = nx.Graph()
g.clear() # Empty the elements on the diagram 

All the operations of building complex network diagrams are basically performed around this g.

2. Nodes

The name of a node can be of any data type. Adding 1 node is

g.add_node(1)
g.add_node("a")
g.add_node("spam")

Adding a group of nodes means building a list of nodes in advance and adding them once, which is 1-dimensional with the operation of adding edges later.

g.add_nodes_from([2,3])
or 
a = [2,3]
g.add_nodes_from(a)

One important point to note here is that for add_node plus a dot, the string is only a node with the name of the whole string added. But for

add_nodes_from Plus 1 Group points, the string represents the addition of each 1 Multiple nodes represented by each character, exp : 
g.add_node("spam") # Added 1 A person named spam Node of 
g.add_nodes_from("spam") # Added 4 Node named s,p,a,m
g.nodes() # You can put the above 5 Print out the nodes to see 

Nodes with 1 group of consecutive numbers starting from 0

H = nx.path_graph(10)
g.add_nodes_from(H) # Will 0~9 Joined node 
# But do not use g.add_node(H)

Delete Node

Same as adding nodes

g.remove_node(node_name)
g.remove_nodes_from(nodes_list)

3. Edge

Edge is composed of tuples corresponding to the name of the node, plus one edge

g.add_edge(1,2)
e = (2,3)
g.add_edge(*e) # Direct g.add_edge(e) The data type is wrong, * Is to take out the elements in the tuple 

Add 1 set of edges

g.add_edges_from([(1,2),(1,3)])
g.add_edges_from([("a","spam") , ("a",2)])

Adding 1 series of continuous edges through nx.path_graph (n)

n = 10
H = nx.path_graph(n)
g.add_edges_from(H.edges()) # Added 0~1,1~2 ... n-2~n-1 Such a n-1 Strip continuous edges 

Delete Edge

The operation of adding edges in the same way

g.remove_edge(edge)
g.remove_edges_from(edges_list)

4. View points and edges on a graph

g.add_node(1)
g.add_node("a")
g.add_node("spam")

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method	explanation
Graph.has_node(n)	Return True if the graph contains the node n.
Graph.__contains__(n)	Return True if n is a node, False otherwise.
Graph.has_edge(u, v)	Return True if the edge (u,v) is in the graph.
Graph.order()	Return the number of nodes in the graph.
Graph.number_of_nodes()	Return the number of nodes in the graph.
Graph.__len__()	Return the number of nodes.
Graph.degree([nbunch, weight])	Return the degree of a node or nodes.
Graph.degree_iter([nbunch, weight])	Return an iterator for (node, degree).
Graph.size([weight])	Return the number of edges.
Graph.number_of_edges([u, v])	Return the number of edges between two nodes.
Graph.nodes_with_selfloops()	Return a list of nodes with self loops.
Graph.selfloop_edges([data, default])	Return a list of selfloop edges.
Graph.number_of_selfloops()	Return the number of selfloop edges.

5. Attribute settings for diagrams

Assign initial attributes to a graph

g = nx.Graph(day="Monday") 
g.graph # {'day': 'Monday'}

Modify the properties of a graph

g.graph['day'] = 'Tuesday'
g.graph # {'day': 'Tuesday'}

6. Point property settings

g.add_node(1)
g.add_node("a")
g.add_node("spam")

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7. Edge property settings

From the introduction of g [1] above, we can see that the attributes of edges are displayed in {}, and we can change the attributes according to this show

g.add_node(1)
g.add_node("a")
g.add_node("spam")

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8. Different types of graphs (directed graph Directed graphs, double edge graph Multigraphs)

Directed graphs

g.add_node(1)
g.add_node("a")
g.add_node("spam")

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Multigraphs

Literally, this complex network diagram allows you to have multiple edges between the same nodes

g.add_node(1)
g.add_node("a")
g.add_node("spam")

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9. Graph traversal

g = nx.Graph()
g.add_weighted_edges_from([(1,2,0.125),(1,3,0.75),(2,4,1.2),(3,4,0.375)])
for n,nbrs in g.adjacency_iter(): #n Represents every 1 A starting point, nbrs Yes 1 Each dictionary, each dictionary 1 Elements contain the attributes of the point connected by the starting point and the edge of the two points 
 print n, nbrs
 for nbr,eattr in nbrs.items():
  # nbr Representation heel n Connected points, eattr Represents the property collection of the edge of these two points, where only the weight If you also set up color , then you can pass eattr['color'] Access to the corresponding color Element 
  data=eattr['weight']
  if data<0.5: print('(%d, %d, %.3f)' % (n,nbr,data))

10. Graph generation and some operations on the graph

The following operations are all methods within the networkx package

subgraph(G, nbunch)  - induce subgraph of G on nodes in nbunch
union(G1,G2)    - graph union
disjoint_union(G1,G2) - graph union assuming all nodes are different
cartesian_product(G1,G2) - return Cartesian product graph
compose(G1,G2)   - combine graphs identifying nodes common to both
complement(G)   - graph complement
create_empty_copy(G)  - return an empty copy of the same graph class
convert_to_undirected(G) - return an undirected representation of G
convert_to_directed(G) - return a directed representation of G

11. Graphic analysis

g.add_node(1)
g.add_node("a")
g.add_node("spam")

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By constructing the weight graph, the shortest distance can be calculated directly and quickly by using dijkstra_path () interface

>>> G=nx.Graph()
>>> e=[('a','b',0.3),('b','c',0.9),('a','c',0.5),('c','d',1.2)]
>>> G.add_weighted_edges_from(e)
>>> print(nx.dijkstra_path(G,'a','d'))
['a', 'c', 'd']

12. Mapping

Here are four ways to construct graphs. Choose one of them

nx.draw(g)
nx.draw_random(g) # Point random distribution 
nx.draw_circular(g) # Distribution formation of points 1 Ring 
nx.draw_spectral(g)

Finally, the graphics are displayed

import matplotlib.pyplot as plt
plt.show()

Save the picture to

nx.draw(g)
plt.savefig("path.png")

Modify node color, edge color

g = nx.cubical_graph()
nx.draw(g, pos=nx.spectral_layout(g), nodecolor='r', edge_color='b')
plt.show()

13. Choice of graphic types

Graph Type	NetworkX Class
简单无向图	Graph()
简单有向图	DiGraph()
有自环	Grap(),DiGraph()
有重边	MultiGraph(), MultiDiGraph()

reference: https://networkx.github. io/documentation/networkx-1. 10/reference/classes. html