今天要分享的是最近小小學的課程，

Basics of NetworkX API, using Twitter network

To get you up and running with the NetworkX API, we will run through some basic functions that let you query a Twitter network that has been pre-loaded for you and is available in the IPython Shell as T. The Twitter network comes from KONECT, and shows a snapshot of a subset of Twitter users. It is an anonymized Twitter network with metadata.

Basic drawing of a network using NetworkX

# Import necessary modules
import matplotlib.pyplot as plt
import networkx as nx

# Draw the graph to screen
nx.draw(T_sub)
plt.show()

Queries on a graph

Specifically, you’re going to look for “nodes of interest" and “edges of interest".The .nodes() method returns a list of nodes, while the .edges() method returns a list of tuples, in which each tuple shows the nodes that are present on that edge.

# Use a list comprehension to get the nodes of interest: noi
noi = [n for n, d in T.nodes(data=True) if d['occupation'] == 'scientist']

# Use a list comprehension to get the edges of interest: eoi
eoi = [(u, v) for u, v, d in T.edges(data=True) if d['date'] < date(2010, 1, 1)]

Specifying a weight on edges

Weights can be added to edges in a graph, typically indicating the “strength" of an edge. In NetworkX, the weight is indicated by the 'weight' key in the metadata dictionary.

# Set the weight of the edge
T.edge[1][10]['weight'] = 2

# Iterate over all the edges (with metadata)
for u, v, d in T.edges(data=True):

    # Check if node 293 is involved
    if 293 in [u, v]:
    
        # Set the weight to 1.1
        T.edge[u][v]['weight'] = 1.1

Checking whether there are self-loops in the graph

As Eric discussed, NetworkX also allows edges that begin and end on the same node; while this would be non-intuitive for a social network graph, it is useful to model data such as trip networks, in which individuals begin at one location and end in another.

# Define find_selfloop_nodes()
def find_selfloop_nodes(G):
    """
    Finds all nodes that have self-loops in the graph G.
    """
    nodes_in_selfloops = []

    # Iterate over all the edges of G
    for u, v in G.edges():
    
    # Check if node u and node v are the same
        if u==v:
            
            # Append node u to nodes_in_selfloops
            nodes_in_selfloops.append(u)
            
    return nodes_in_selfloops

Visualizing using Matrix plots

nxviz is a package for visualizing graphs in a rational fashion. A corresponding nx.from_numpy_matrix(A) allows one to quickly create a graph from a NumPy matrix. The default graph type is Graph(); if you want to make it a DiGraph(), that has to be specified using the create_using keyword argument, e.g. (nx.from_numpy_matrix(A, create_using=nx.DiGraph)).

# Import nxviz
import nxviz as nv

# Create the MatrixPlot object: m
m = nv.MatrixPlot(T)

# Draw m to the screen
m.draw()

# Display the plot
plt.show()

# Convert T to a matrix format: A
A = nx.to_numpy_matrix(T)

# Convert A back to the NetworkX form as a directed graph: T_conv
T_conv = nx.from_numpy_matrix(A, create_using=nx.DiGraph())

# Check that the `category` metadata field is lost from each node
for n, d in T_conv.nodes(data=True):
    assert 'category' not in d.keys()

Visualizing using Circos plots

Circos plots are a rational, non-cluttered way of visualizing graph data, in which nodes are ordered around the circumference in some fashion, and the edges are drawn within the circle that results, giving a beautiful as well as informative visualization about the structure of the network

# Import necessary modules
import matplotlib.pyplot as plt
from nxviz import CircosPlot

# Create the CircosPlot object: c
c = CircosPlot(T)

# Draw c to the screen
c.draw()

# Display the plot
plt.show()

Visualizing using Arc plots

Two keyword arguments that you will try here are node_order='keyX' and node_color='keyX', in which you specify a key in the node metadata dictionary to color and order the nodes by.

# Import necessary modules
import matplotlib.pyplot as plt
from nxviz import ArcPlot

# Create the un-customized ArcPlot object: a
a = ArcPlot(T)

# Draw a to the screen
a.draw()

# Display the plot
plt.show()

# Create the customized ArcPlot object: a2
a2 = ArcPlot(T,node_order='category',node_color='category')

# Draw a2 to the screen
a2.draw()

# Display the plot
plt.show()

 

resources: DataCamp, network analysis in python I