nx.number_strongly_connected_components(
artistGraph)) + " strongly connected components."
my_component = artistGraph
for component in nx.strongly_connected_component_subgraphs(artistGraph):
if search.id in component:
my_component = component
print "This artist's clique currently contains " + str(
len(artistGraph)) + " artists."
# Go through the graph and compute each PR until it converges.
iterations = 10
print "Computing PageRank on your searched artist..."
computePR(my_component, 0.85, iterations)
prList = []
for artist in my_component.nodes():
prList.append((artist, my_component.node[artist]['currPR']))
prList.sort(key=lambda tup: tup[1]) # Sort the list in place
prList.reverse() # order by descending PR
print("Here are some artists similar to " + str(search.username))
for item in prList[0:10]:
artist = scac.id2username(item[0])
print artist, item[1]
print "Artist interpreted as: %s" % search.username
# need to compute all neighbors in given graph selection before we can compute the
# pr of each node.
print "="*20
my_component = artistGraph
for component in nx.strongly_connected_component_subgraphs(artistGraph):
if search.id in component:
my_component = component
# Go through the graph and compute each PR until it converges.
iterations = 10
print "Computing PageRank on your searched artist..."
computePR(my_component , 0.85, iterations)
prList = []
for artist in my_component.nodes():
prList.append((artist, my_component.node[artist]['currPR']))
prList.sort(key = lambda tup: tup[1]) # Sort the list in place
prList.reverse() # order by descending PR
print ("Here are some artists similar to " + str(search.username) )
for item in prList[0:10]:
artist = scac.id2username(item[0])
print artist, item[1]
# need to compute all neighbors in given graph selection before we can compute the
# pr of each node.
depth = 2
i = 0
for t in range(depth):
print "Iteration " + str(t)
current_artists = artistDict.values()
for artist in current_artists:
print "Artist " + str(i) + " of " + str(len(current_artists))
getNeighbors(artist)
i += 1
# Go through the graph and compute each PR until it converges.
iterations = 10
computePR(artistDict, 0.85, iterations)
prList = []
for artist in artistDict.values():
prList.append((artist.id, artist.pr[10]))
prList.sort(key = lambda tup: tup[1]) # Sort the list in palce
prList.reverse() # order by descending PR
print ("Here are some artists that " + str(aoi.id) + " is interested in:")
try:
print aoi.inNeighbors
print aoi.outNeighbors
print "The PR of this artist is: " + str(aoi.pr[t])
else:
print "\t", "Artist ID %s is not query-able" % profile
unavailable_profiles.append(profile)
print "The profile graph currently contains " + str(len(profileGraph.nodes())) + " profiles."
print "Here are their connections."
print_graph(profileGraph)
print "The profile graph currently contains " + str(nx.number_strongly_connected_components(profileGraph)) + " strongly connected components."
# Go through the graph and compute each PR until it converges.
iterations = 10
print "Computing PageRank on our profileGraph..."
computePR(profileGraph, 0.85, iterations)
prList = []
for profile in profileGraph.nodes():
prList.append((profile, profileGraph.node[profile]['currPR']))
prList.sort(key = lambda tup: tup[1]) # Sort the list in place
prList.reverse() # order by descending PR
print ("Here are some profiles similar to " + str(search.username) )
for item in prList[0:10]:
profile = scac.id2username(item[0])
print profile, item[1]
print "\t", username + " has " + str(len(followings)) + " followings"
print "\t", username + " follows " + ", ".join(map(lambda x: scac.id2username(x), followings))
print "\t", username + " has " + str(len(followers)) + " followers"
print "\t", username + " is followed by " + ", ".join(map(lambda x: scac.id2username(x), followers))
print "-" * 40
print "The artist graph currently contains " + str(
nx.number_strongly_connected_components(artistGraph)
) + " strongly connected components."
nx.write_graphml(artistGraph, "artistGraph.graphml")
# Go through the graph and compute each PR until it converges.
iterations = 10
print "Computing PageRank on our artistGraph..."
computePR(artistGraph, 0.85, iterations)
prList = []
for artist in artistGraph.nodes():
prList.append((artist, artistGraph.node[artist]["currPR"]))
prList.sort(key=lambda tup: tup[1]) # Sort the list in place
prList.reverse() # order by descending PR
print ("Here are some artists similar to " + str(search.username))
for item in prList[0:10]:
artist = scac.id2username(item[0])
print artist, item[1]
else:
print "\t", "Artist ID %s is not query-able" % profile
unavailable_profiles.append(profile)
print "The profile graph currently contains " + str(len(profileGraph.nodes())) + " profiles."
print "Here are their connections."
print_graph(profileGraph)
print "The profile graph currently contains " + str(nx.number_strongly_connected_components(profileGraph)) + " strongly connected components."
# Go through the graph and compute each PR until it converges.
iterations = 10
print "Computing PageRank on our profileGraph..."
computePR(profileGraph, 0.85, iterations)
prList = []
for profile in profileGraph.nodes():
prList.append((profile, profileGraph.node[profile]['currPR']))
prList.sort(key = lambda tup: tup[1]) # Sort the list in place
prList.reverse() # order by descending PR
print ("Here are some profiles similar to " + str(search.username) )
for item in prList[0:10]:
profile = scac.id2username(item[0])
print profile, item[1]