def pickRandEdges():
global externalDG
global clusterDG
open('OutputExp6/clusterOpinions.csv', 'w').close()
open('OutputExp6/nonClusterOpinions.csv', 'w').close()
print 'creating Cluster graph with PRN'
clusterDG = runPRN()
print 'creating nonCluster graph'
externalDG = readDotFile('advogato-graph-latest.dot')
clusterEdges = clusterDG.edges()
clusterNodes = clusterDG.nodes()
externalDG.remove_edges_from(clusterEdges)
externalDG.remove_nodes_from(clusterNodes)
externalEdges = externalDG.edges()
for i in range(0,1000):
if len(clusterEdges) < 2:
print "cluster too small to sample from, aborting..."
break
randClusterEdge = random.sample(clusterEdges, 1)
randExtEdge = random.sample(externalEdges, 1)
clusterOpn = TVSLTran(clusterDG[randClusterEdge[0][0]][randClusterEdge[0][1]]['level'])
externalOpn = TVSLTran(externalDG[randExtEdge[0][0]][randExtEdge[0][1]]['level'])
with open('OutputExp6/clusterOpinions.csv', 'a') as csvfile:
toWrite = csv.writer(csvfile, delimiter = ',')
toWrite.writerow([clusterOpn[0], clusterOpn[1], clusterOpn[2], clusterOpn[3]])
with open('OutputExp6/nonClusterOpinions.csv', 'a') as csvfile:
toWrite = csv.writer(csvfile, delimiter = ',')
toWrite.writerow([externalOpn[0], externalOpn[1], externalOpn[2], externalOpn[3]])
def runPRN():
#DG = readDotFile('/home/loenix/Documents/advogato_graph/advogato-graph-2014-03-16.dot')
DG = readDotFile('advogato-graph-latest.dot')
#DG = nx.DiGraph(nx.read_dot('/home/loenix/Documents/advogato_graph/advogato-graph-2014-03-16.dot'))
Eps = 0.000001 #set up epsilon
alpha = 0.15 # set alpha
#pick up a nodes far enough from the seed
#so that the subgraph on which APPR run will
#will be large enough
numHops = 4
rand = random.randint(0,len(DG.nodes())-1)
Seed = DG.nodes()[rand]
remoteSeed = getTrustorsOfExactHop(DG, Seed, numHops)
while remoteSeed == 0 :
Seed = DG.nodes()[rand]
remoteSeed = getTrustorsOfExactHop(DG, Seed, numHops)
#now got a seed which has 4 hop neighbor, run APPR
#print('seed is:' + Seed)
#print('nb of seed is: ' + str(DG.neighbors(Seed)))
#since the algr works on undirected graph
DG.to_undirected()
PR = PageRankNibble(DG, Seed, alpha, Eps)
#using the ranked nodes to form a subgraph.
H = DG.subgraph(PR)
nx.write_dot(H, 'pprResult.dot')
return H
# Creates a distribution of the shortest distance between nodes
# Samples 1000 nodes
# file[0] contains the number of nodes where no path exists
def smallWorldProblem(numberOfReps=1000):
distribution = []
for i in range(0, 50):
distribution.append(0)
for source in DG.nodes()[0:numberOfReps]:
for D in DG.nodes():
if source != D:
if nx.has_path(DG, source, D) == False:
distribution[0] += 1
else:
distribution[nx.shortest_path_length(DG, source, D)] += 1
distWrite(distribution, "OutputExp2/smallworld.txt")
DG = readDotFile("data.dot")
if os.path.exists('OutputExp2') == False:
print "Making the directory OutputExp2"
os.mkdir("OutputExp2")
testTrustPropogation()
testTrustComposing()
smallWorldProblem()
#need a sample of 1000 opinions. ideally, running for 50000 iterations will produce a list
# that has at least 1000 opinions with expected belief over 0.75
for i in range(0, numberOfReps):
newPair = random.sample(DG.nodes(), 2)
if newPair not in pairsList:
if nx.has_path(DG, newPair[0], newPair[1]):
pairOpnDG = nx.DiGraph()
pathLength = nx.shortest_path_length(DG, newPair[0], newPair[1])
paths = nx.all_simple_paths(DG, source=newPair[0], target=newPair[1], cutoff=pathLength+ additionToPathLength)
for path in paths:
nodeList = []
for i in range(0, len(path)-1):
pairOpnDG.add_edge(path[i], path[i+1], level=DG[path[i]][path[i+1]]['level'])
pairOpinion = TVSLAlgr(pairOpnDG, newPair[0], newPair[1], pathLength+additionToPathLength, 0)
print pathLength
print pairOpinion
with open('OutputExp4/opinionsList.csv', 'a') as csvfile:
toWrite = csv.writer(csvfile, delimiter = ',')
toWrite.writerow([pathLength, pairOpinion[0], pairOpinion[1], pairOpinion[2], pairOpinion[3]])
if os.path.exists('OutputExp4') == False:
print "Making the directory OutputExp4"
os.mkdir("OutputExp4")
DG = readDotFile("data.dot")
testCocitationCouplingAndTransitivity()
makeHighExpBeliefSample()
and (oldDG.has_edge(B, C) == False)
and cocitationCounter < desiredTriangles):
print "cocitation"
cocitationCounter += 1
XLevel = newDG[A][B]['level']
YLevel = newDG[A][C]['level']
ZLevel = newDG[B][C]['level']
writeTriangleOfTrust(XLevel, YLevel, ZLevel,
"OutputExp5/CoCitation.csv")
elif ((oldDG.has_edge(A, B) == False) and oldDG.has_edge(A, C)
and oldDG.has_edge(B, C) and couplingCounter < desiredTriangles):
print "couple"
couplingCounter += 1
XLevel = newDG[B][C]['level']
YLevel = newDG[A][C]['level']
ZLevel = newDG[A][B]['level']
writeTriangleOfTrust(XLevel, YLevel, ZLevel,
"OutputExp5/Coupling.csv")
if os.path.exists('OutputExp5') == False:
print "Making the directory OutputExp5"
os.mkdir("OutputExp5")
oldDG = readDotFile("old.dot")
newDG = readDotFile("data.dot")
newDG = newDG.subgraph(oldDG.nodes())
testCocitationCouplingAndTransitivityOverTime()
clusterOpn = TVSLTran(clusterDG[randClusterEdge[0][0]][randClusterEdge[0][1]]['level'])
externalOpn = TVSLTran(externalDG[randExtEdge[0][0]][randExtEdge[0][1]]['level'])
with open('OutputExp6/clusterOpinions.csv', 'a') as csvfile:
toWrite = csv.writer(csvfile, delimiter = ',')
toWrite.writerow([clusterOpn[0], clusterOpn[1], clusterOpn[2], clusterOpn[3]])
with open('OutputExp6/nonClusterOpinions.csv', 'a') as csvfile:
toWrite = csv.writer(csvfile, delimiter = ',')
toWrite.writerow([externalOpn[0], externalOpn[1], externalOpn[2], externalOpn[3]])
if os.path.exists('OutputExp6') == False:
print "Making the directory OutputExp6"
os.mkdir("OutputExp6")
DG = readDotFile('advogato-graph-latest.dot')
H = runPRN()
pickRandEdges()
print "within"
withinCluster(DG, H, 2)
print "out"
outOfCluster(DG, H, 2)
print "done"
elif (oldDG.has_edge(A,B) and oldDG.has_edge(A,C) and
(oldDG.has_edge(B,C) == False) and cocitationCounter < desiredTriangles):
print "cocitation"
cocitationCounter+=1
XLevel = newDG[A][B]['level']
YLevel = newDG[A][C]['level']
ZLevel = newDG[B][C]['level']
writeTriangleOfTrust(XLevel,YLevel,ZLevel,"OutputExp5/CoCitation.csv")
elif ((oldDG.has_edge(A,B)==False) and oldDG.has_edge(A,C) and
oldDG.has_edge(B,C) and couplingCounter < desiredTriangles):
print "couple"
couplingCounter+=1
XLevel = newDG[B][C]['level']
YLevel = newDG[A][C]['level']
ZLevel = newDG[A][B]['level']
writeTriangleOfTrust(XLevel,YLevel,ZLevel,"OutputExp5/Coupling.csv")
if os.path.exists('OutputExp5') == False:
print "Making the directory OutputExp5"
os.mkdir("OutputExp5")
oldDG = readDotFile("old.dot")
newDG = readDotFile("data.dot")
newDG = newDG.subgraph(oldDG.nodes())
testCocitationCouplingAndTransitivityOverTime()