def runthis():
fin = open("common.txt")
String = fin.readlines()
fin.close()
for i in range(len(String)):
String[i] = String[i][:-1]
Mapping = {'a': 1, 'b': 2, 'c':3, 'd':4, 'e':5, 'f':6, 'g':7, 'h':8, 'i':9, 'j':10, 'k':11, 'l':12, 'm':13, 'n':14, 'o':15, 'p':16, 'q':17, 'r':18, 's':19, 't':20, 'u':21, 'v':22, 'w':23, 'x':24, 'y':25, 'z':26}
'''
numericals = []
for i in range(len(String)):
numericals.append((String[i], Mapping[String[i][0]], Mapping[String[i][1]], Mapping[String[i][2]]))
print numericals
'''
G = nx.Graph()
for i in range(len(String)):
for j in range(i+1, len(String)):
if String[i][0] == String[j][0] and String[i][1] == String[j][1] and String[i][2] != String[j][2] or String[i][0] == String[j][0] and String[i][1] != String[j][1] and String[i][2] == String[j][2] or String[i][0] != String[j][0] and String[i][1] == String[j][1] and String[i][2] == String[j][2]:
G.add_edge(String[i], String[j])
fin = open("wordmorph.Mygml", 'w')
string = pickle.dumps(G)
fin.write(string)
fin.close()
AtoB.createRealWorld("wordmorph")
print "Number of words:", len(String)
print "Drawing Graph..."
nx.draw(G)
plt.show()
def runthis():
fin = open('p2p-Gnutella08.txt','r')
String = fin.read()
fin.close()
ConnectionList = String.rsplit()[29:]
EdgeList = []
for i in range(0,len(ConnectionList),2):
EdgeList.append((int(ConnectionList[i]), int(ConnectionList[i+1])))
G = nx.Graph()
G.add_edges_from(EdgeList)
G = nx.subgraph(G,range(500))
fin = open("gnutella.Mygml", 'w')
string = pickle.dumps(G)
fin.write(string)
fin.close()
AtoB.createRealWorld("gnutella")
#n = 200
edgeProb = 0.3
degree = 3
#name = "gnutella"
#trials = 10000
start = 100
end = 401
step = 20
x = []
plainAdamic = []
twoWayAdamic = []
for n in range(start, end, step):
AtoB.createScaleFreeNetwork(n, degree)
G = nx.read_gpickle("SFN_" + str(n) + "_" + str(degree) + ".gpickle")
result = AtoB.simple_query(G)
plainAdamic.append(result[0])
twoWayAdamic.append(result[1])
x.append(n)
plt.plot(x, plainAdamic)
plt.plot(x, twoWayAdamic)
plt.xlabel("Number of Nodes")
plt.ylabel("Time Taken")
plt.title("Average time taken to compute the approximate shortest path for all the nC2 pairs")
plt.show()
#AtoB.createErdos(n,edgeProb)
import numpy
import math
import AtoB
import matplotlib.pyplot as plt
import random
import networkx as nx
import pdb
import pickle
import generateGraph
print "Word Morph Game Network:"
print "The Path Concatenation Algorithm (PCA) has been applied to the above graph"
while(True):
word1 = raw_input("Enter word 1: ")
word2 = raw_input("Enter word 2: ")
AtoB.main(0, "RW", 'wordmorph', [word1,word2])
condition = raw_input("\nContinue? (Y\N): ")
if condition != 'Y':
break
import numpy
import math
import AtoB
import matplotlib.pyplot as plt
import random
import networkx as nx
import pdb
import pickle
n = 200
edgeProb = 0.3
degree = 3
# name = "gnutella"
# AtoB.createErdos(n,edgeProb)
# G = nx.read_gpickle("EG_" + str(n) + "_" + str(edgeProb) + ".gpickle")
# AtoB.createScaleFreeNetwork(n, degree)
# G = nx.read_gpickle("SFN_" + str(n) + "_" + str(degree) + ".gpickle")
AtoB.createRealWorld(name)
G = nx.read_gpickle(str(name) + ".gpickle")
# AtoB.simple_query(G)
AtoB.comparison_query(G)
import numpy
import math
import AtoB
import matplotlib.pyplot as plt
import random
import networkx as nx
import pdb
import pickle
import generateGraph
print "Gnutella P2P Network:"
print "Source: http://snap.stanford.edu/data/p2p-Gnutella08.html"
print "The Path Concatenation Algorithm (PCA) has been applied to an induced subgraph of the above graph consisting of 500 nodes"
while(True):
node1 = int(input("Enter node 1: "))
node2 = int(input("Enter node 2: "))
AtoB.main(0, "RW", 'gnutella', [node1,node2])
condition = raw_input("\nContinue? (Y\N): ")
if condition != 'Y':
break
import numpy
import math
import AtoB
import matplotlib.pyplot as plt
import random
import networkx as nx
import pdb
import pickle
name = "gnutella"
trials = 10000
G = nx.read_gpickle(str(name) + ".gpickle")
result = AtoB.simple_query(G, trials)
print "The ratio of the time intervals taken to perform One-way Adamic walk and Two-way Adamic walk is " + str(float(result[0])/result[1])
print "Hence, Two-way Adamic walk is faster than One-way Adamic walk by a factor of " + str(float(result[1])/result[0])
ind = numpy.arange(2)
width = 0.15
plt.bar(ind, (result[0], result[1]), width)
plt.xticks(ind+width/2., ('One-way Adamic', 'Two-way Adamic') )
plt.xlabel("Number of Nodes")
plt.ylabel("Time Taken")
plt.title("Average time taken to compute the approximate shortest path for all the nC2 pairs")
plt.show()
#AtoB.createErdos(n,edgeProb)
#G = nx.read_gpickle("EG_" + str(n) + "_" + str(edgeProb) + ".gpickle")
import numpy
import math
import AtoB
import matplotlib.pyplot as plt
import random
import networkx as nx
import pdb
import pickle
import generateGraph
#node1 = int(input("Enter node 1: "))
#node2 = int(input("Enter node 2: "))
#generateGraph.runthis()
#AtoB.main(0, "RW", 'gnutella', [node1,node2])
AtoB.main(0, "RW", 'gnutella', [0,1])
import numpy
import math
import AtoB
import matplotlib.pyplot as plt
import random
import networkx as nx
import pdb
import pickle
n = 200
edgeProb = 0.3
degree = 3
name = "gnutella"
trials = 10000
#AtoB.createErdos(n,edgeProb)
#G = nx.read_gpickle("EG_" + str(n) + "_" + str(edgeProb) + ".gpickle")
#AtoB.createScaleFreeNetwork(n, degree)
#G = nx.read_gpickle("SFN_" + str(n) + "_" + str(degree) + ".gpickle")
#AtoB.createRealWorld(name)
G = nx.read_gpickle(str(name) + ".gpickle")
AtoB.simple_query(G, trials)
#AtoB.comparison_query(G)
#name = "gnutella"
#trials = 10000
start = 100
end = 401
step = 20
x = []
oneWayRandom = []
twoWayRandom = []
oneWayAdamic = []
twoWayAdamic = []
for n in range(start, end, step):
AtoB.createScaleFreeNetwork(n, degree)
G = nx.read_gpickle("SFN_" + str(n) + "_" + str(degree) + ".gpickle")
#AtoB.createErdos(n,edgeProb)
#G = nx.read_gpickle("EG_" + str(n) + "_" + str(edgeProb) + ".gpickle")
result = AtoB.comparison_query(G)
oneWayRandom.append(result[0])
twoWayRandom.append(result[1])
oneWayAdamic.append(result[2])
twoWayAdamic.append(result[3])
x.append(n)
plt.plot(x, oneWayRandom)
plt.plot(x, twoWayRandom)
plt.plot(x, oneWayAdamic)
plt.plot(x, twoWayAdamic)
plt.xlabel("Number of Nodes")
import numpy
import math
import AtoB
import matplotlib.pyplot as plt
import random
import networkx as nx
import pdb
import pickle
name = "gnutella"
trials = 100
G = nx.read_gpickle(str(name) + ".gpickle")
result = AtoB.comparison_query(G, trials)
ind = numpy.arange(4)
width = 0.15
plt.bar(ind, (result[0], result[1], result[2], result[3]), width)
plt.xticks(ind+width/2., ('One-way Random walk', 'Two-way Random walk', 'One-way Adamic walk', 'Two-way Adamic walk') )
plt.xlabel("Number of Nodes")
plt.ylabel("Average of (length of approx path / length of shortest path) over nC2 pairs")
#plt.title("Average time taken to compute the approximate shortest path for all the nC2 pairs")
plt.show()
#AtoB.createRealWorld(name)
#G = nx.read_gpickle(str(name) + ".gpickle")
#AtoB.simple_query(G)
#AtoB.comparison_query(G)
import numpy
import math
import AtoB
import matplotlib.pyplot as plt
import random
import networkx as nx
import pdb
import pickle
numOfNodes = 100
degree = 3
edgeProb = 0.3
AtoB.createErdos(numOfNodes,edgeProb)
G = nx.read_gpickle("EG_" + str(numOfNodes) + "_" + str(edgeProb) + ".gpickle")
#AtoB.createScaleFreeNetwork(numOfNodes, degree)
#G = nx.read_gpickle("SFN_" + str(numOfNodes) + "_" + str(degree) + ".gpickle")
AtoB.Navigate(G)
