rkwitt · December 15, 2015 18:49
diff --git a/kw-techtip-04-03-2013-p2.py b/kw-techtip-04-03-2013-p2.py
 """kw-techtip-04-03-2013-p2.py

 Fine-structure analysis of a graph.

 Usage
 -----
    
 Assuming that the file "graph.txt" generated by kw-techtip-04-03-2013-p1.py
 resides in "/tmp", run
    
 $ python kw-techtip-04-03-2013-p2.py

 to see what the feature matrix for the graph looks like.
 """


 import pickle
 import numpy as np
 import networkx as nx
 import matplotlib.pyplot as plt


 def visualize_subgraph(g, n):
    """Writes subgraph to .png file.
    """
    
    plt.title("Subgraph (Node: %.4d)" % n)
    colors = ['white'] * len(sg)
    colors[np.where(np.asarray(sg.nodes()) == n)[0]] = 'b'
    nx.draw(g, node_color=colors)
    plt.savefig("/tmp/sg-%.4d.pdf" % n)
    plt.close()


 # Define the graph features
 attr_list = [ #Average vertex degree
             lambda g : np.mean([e for e in g.degree().values()]),                 
             # Spectral radius (i.e., largest eigenvalue of adjacency matrix)
             lambda g : np.abs(nx.adjacency_spectrum(g))[0]]
 
 # Load graph from disk
 G = pickle.load(open("/tmp/graph.txt"))
 
 # Run All-Shortest path algorithm
 sps = nx.floyd_warshall_numpy(G)

 # Consider all neighbors reachable via 2 hops
 hops = 2 

 V = np.zeros([len(G),len(attr_list)])
 for n in G.nodes():
    # Select n-th row in shortest-path matrix
    nth_row = np.array(sps[n,:]).ravel()
    # Get all neighbors within the specified number of hops
    within_radius = np.where(nth_row <= hops)
    # Extract subgraph centered on n-th vertex
    sg = G.subgraph(within_radius[0])
    # Compute subragph features
    V[n,:] = np.asarray([attr_fun(sg) for attr_fun in attr_list]) 
    # Visualize the subgraph
    visualize_subgraph(sg, n)

 print V
 print "Feature matrix shape: (%d,%d)" % V.shape
	"""kw-techtip-04-03-2013-p2.py

	Fine-structure analysis of a graph.

	Usage
	-----

	Assuming that the file "graph.txt" generated by kw-techtip-04-03-2013-p1.py
	resides in "/tmp", run

	$ python kw-techtip-04-03-2013-p2.py

	to see what the feature matrix for the graph looks like.
	"""


	import pickle
	import numpy as np
	import networkx as nx
	import matplotlib.pyplot as plt


	def visualize_subgraph(g, n):
	"""Writes subgraph to .png file.
	"""

	plt.title("Subgraph (Node: %.4d)" % n)
	colors = ['white'] * len(sg)
	colors[np.where(np.asarray(sg.nodes()) == n)[0]] = 'b'
	nx.draw(g, node_color=colors)
	plt.savefig("/tmp/sg-%.4d.pdf" % n)
	plt.close()


	# Define the graph features
	attr_list = [ #Average vertex degree
	lambda g : np.mean([e for e in g.degree().values()]),
	# Spectral radius (i.e., largest eigenvalue of adjacency matrix)
	lambda g : np.abs(nx.adjacency_spectrum(g))[0]]

	# Load graph from disk
	G = pickle.load(open("/tmp/graph.txt"))

	# Run All-Shortest path algorithm
	sps = nx.floyd_warshall_numpy(G)

	# Consider all neighbors reachable via 2 hops
	hops = 2

	V = np.zeros([len(G),len(attr_list)])
	for n in G.nodes():
	# Select n-th row in shortest-path matrix
	nth_row = np.array(sps[n,:]).ravel()
	# Get all neighbors within the specified number of hops
	within_radius = np.where(nth_row <= hops)
	# Extract subgraph centered on n-th vertex
	sg = G.subgraph(within_radius[0])
	# Compute subragph features
	V[n,:] = np.asarray([attr_fun(sg) for attr_fun in attr_list])
	# Visualize the subgraph
	visualize_subgraph(sg, n)

	print V
	print "Feature matrix shape: (%d,%d)" % V.shape