def compute_user_pagerank(network, user, directed_networks, weighted):
if len(network.es) == 0:
return 1
options = igraph.ARPACKOptions()
options.mxiter = 100000
# Igraph 0.7 has a bad bug! It does not give pagerank of a specific user. It
# only returns pagerank of first user in the graph, no matter what the
# vertices argument is. So we get a list of all pageranks, then query in the
# list with the index of node with name user
user_index = network.vs.find(user).index
if weighted:
# if weights are requested, we should use the original weights rather than their inverse
# (in contrast to closeness). In case of pagerank, large edge weights are good for the
# incoming node. They give it more credit.
weights = network.es['weight']
else:
weights = None
user_pagerank = network.pagerank(directed=directed_networks,
damping=0.85,
weights=weights,
implementation="prpack",
arpack_options=options)[user_index]
if user_pagerank > 1:
sys.stderr.write('Error: Pagerank of user %s is greater than one: %f' %
(user, user_pagerank))
# Igraph returns tiny non-zero values for page ranks that should be really zero.
# manually zero them out.
if user_pagerank < pow(10, -15):
user_pagerank = 0
return utils.round_to_sigfigs(user_pagerank, NUM_SIGFIGS)
def compute_user_satoshi_pagerank(network, user, weighted):
if len(network.es) == 0:
return 1
if 'satoshi' not in network.vs["name"]:
return 0
# increase the pagerank computation max number of iterations. sometimes arpack does not
# converge.
options = igraph.ARPACKOptions()
options.mxiter = 100000
# Igraph 0.7 has a bad bug! It does not give pagerank of a specific user. It
# only returns pagerank of first user in the graph, no matter what the
# vertices argument is. So we get a list of all pageranks, then query in the
# list with the index of node with name user
user_index = network.vs.find(user).index
if weighted:
# if weights are requested, we should use the original weights rather than their inverse
# (in contrast to closeness). In case of pagerank, large edge weights are good for the
# incoming node. They give it more credit.
weights = network.es['weight']
else:
weights = None
# Vertices should be the active user whose page rank we want to compute. directed
# should be true so that directed paths are considered. damping is the probability
# that we reset the random walk on Satoshi at each step. reset_vertices should only
# contain Satoshi, since we are interested in flow from Satoshi. weights should be
# the 'weight' attribute so that weights are used in page rank computation.
user_satoshi_pagerank = network.personalized_pagerank(
directed=True,
damping=0.85,
reset_vertices='satoshi',
weights=weights,
implementation="prpack",
arpack_options=options)[user_index]
if user_satoshi_pagerank > 1:
sys.stderr.write(
'Error: Satoshi pagerank of user %s is greater than one: %f' %
(user, user_satoshi_pagerank))
# Igraph returns tiny non-zero values for page ranks that should be really zero.
# manually zero them out.
if user_satoshi_pagerank < pow(10, -15):
user_satoshi_pagerank = 0
return utils.round_to_sigfigs(user_satoshi_pagerank, NUM_SIGFIGS)
def compute_mean_median_std_pagerank(network, directed_networks, weighted):
if len(network.es) == 0:
return [1] * 3
options = igraph.ARPACKOptions()
options.mxiter = 100000
if weighted:
weights = network.es['weight']
else:
weights = None
s = network.pagerank(directed=directed_networks,
damping=0.85,
weights=weights,
implementation="prpack",
arpack_options=options)
return [numpy.mean(s), numpy.median(s), numpy.std(s)]
def compute_mean_median_std_satoshi_pagerank(network, directed_networks,
weighted):
if len(network.es) == 0:
return [1] * 3
if 'satoshi' not in network.vs["name"]:
return [0] * 3
options = igraph.ARPACKOptions()
options.mxiter = 100000
if weighted:
weights = network.es['weight']
else:
weights = None
s = network.personalized_pagerank(directed=directed_networks,
damping=0.85,
reset_vertices='satoshi',
weights=weights,
implementation="prpack",
arpack_options=options)
return [numpy.mean(s), numpy.median(s), numpy.std(s)]