def test():
G = NX.DiGraph()
edges = [(1,2), (1,3),
(3,1), (3,2), (3,5),
(4,5), (4,6),
(5,4), (5,6),
(6,4)]
G.add_edges_from(edges)
M = page_rank.google_matrix(G, alpha=0.9)
e, ev = numpy.linalg.eig(M.T)
p = numpy.array(ev[:,0] / ev[:,0].sum())[:,0]
print "exact ", p
pr = page_rank.page_rank(G,alpha=0.9,tol=1.0e-8)
print "networkx", pr.values()
np = page_rank.page_rank_numpy(G,alpha=0.9)
print "numpy ", np
try:
ns = page_rank.page_rank_scipy(G,alpha=0.9)
print "scipy ", ns
except Error:
print "scipy not working"
def test():
G = NX.DiGraph()
edges = [(1, 2), (1, 3), (3, 1), (3, 2), (3, 5), (4, 5), (4, 6), (5, 4),
(5, 6), (6, 4)]
G.add_edges_from(edges)
M = page_rank.google_matrix(G, alpha=0.9)
e, ev = numpy.linalg.eig(M.T)
p = numpy.array(ev[:, 0] / ev[:, 0].sum())[:, 0]
print "exact ", p
pr = page_rank.page_rank(G, alpha=0.9, tol=1.0e-8)
print "networkx", pr.values()
np = page_rank.page_rank_numpy(G, alpha=0.9)
print "numpy ", np
try:
ns = page_rank.page_rank_scipy(G, alpha=0.9)
print "scipy ", ns
except Error:
print "scipy not working"
#G=networkx.generators.random_graphs.erdos_renyi_graph(10,0.15)
G = networkx.read_dot(a.filepath)
# G = networkx.read_dot("/home/phauly/datasets/Advogato/graph.dot")
print "network is of type=" + repr(type(G))
G=networkx.DiGraph(G) #cast a XDiGraph into a Graph, maybe this is not needed for pagerank to work, maybe it is needed
#G.ban_multiedges() #pagerank doesn't work on multiedge graphs
print "Finished reading the dataset --> Number of nodes of dataset = %s" % len(G.nodes())
#print G.edges()
print "Do you want to run numpy.linalg.eig? (y/n) [It might be very heavy]"
answer=sys.stdin.readline()[:-1]
if answer == "y":
import numpy
M=NX_future_page_rank.google_matrix(G,alpha=0.9)
e,ev=numpy.linalg.eig(M.T)
p=numpy.array(ev[:,0]/ev[:,0].sum())[:,0]
print "exact ", p
print "Do you want to run page_rank? (y/n) [It might be very heavy]"
answer=sys.stdin.readline()[:-1]
if answer == "y":
pr = NX_future_page_rank.page_rank(G,alpha=0.9,tol=1.0e-8)
print "networkx", pr.values()
print "Do you want to run page_rank numpy? (y/n) [It might be very heavy]"
answer=sys.stdin.readline()[:-1]
if answer == "y":
np=NX_future_page_rank.page_rank_numpy(G,alpha=0.9)
print "numpy ", np
print "network is of type=" + repr(type(G))
G = networkx.DiGraph(
G
) #cast a XDiGraph into a Graph, maybe this is not needed for pagerank to work, maybe it is needed
#G.ban_multiedges() #pagerank doesn't work on multiedge graphs
print "Finished reading the dataset --> Number of nodes of dataset = %s" % len(
G.nodes())
#print G.edges()
print "Do you want to run numpy.linalg.eig? (y/n) [It might be very heavy]"
answer = sys.stdin.readline()[:-1]
if answer == "y":
import numpy
M = NX_future_page_rank.google_matrix(G, alpha=0.9)
e, ev = numpy.linalg.eig(M.T)
p = numpy.array(ev[:, 0] / ev[:, 0].sum())[:, 0]
print "exact ", p
print "Do you want to run page_rank? (y/n) [It might be very heavy]"
answer = sys.stdin.readline()[:-1]
if answer == "y":
pr = NX_future_page_rank.page_rank(G, alpha=0.9, tol=1.0e-8)
print "networkx", pr.values()
print "Do you want to run page_rank numpy? (y/n) [It might be very heavy]"
answer = sys.stdin.readline()[:-1]
if answer == "y":
np = NX_future_page_rank.page_rank_numpy(G, alpha=0.9)
print "numpy ", np
def exactPR(G):
M = page_rank.google_matrix(G, alpha=0.9)
e, ev = numpy.linalg.eig(M.T)
p = numpy.array(ev[:,0] / ev[:,0].sum())[:,0]
return p
def exactPR(G):
M = page_rank.google_matrix(G, alpha=0.9)
e, ev = numpy.linalg.eig(M.T)
p = numpy.array(ev[:, 0] / ev[:, 0].sum())[:, 0]
return p