def hubs_and_authorities_score(self, MaxIter=20):
'''
Computes the Hubs and Authorities score of every node in Graph
return tuple of hubs score and authorrities score
:param MaxIter: Maximum number of iterations.
'''
snap = self.snap
ret1 = []
ret2 = []
NIdHubH = snap.TIntFlt64H()
NIdAuthH = snap.TIntFlt64H()
snap.GetHits(self.graph, NIdHubH, NIdAuthH, MaxIter)
for item in NIdHubH:
ret1.append((item, NIdHubH[item]))
for item in NIdAuthH:
ret2.append((item, NIdAuthH[item]))
return ret1, ret2
def page_rank_score(self, C=0.85, Eps=1e-4, MaxIter=100):
'''
Computes the PageRank score of every node in Graph
:param C: Damping factor.
:param Eps: Convergence difference.
:param MaxIter: Maximum number of iterations.
'''
snap = self.snap
ret = []
PRankH = snap.TIntFlt64H()
snap.GetPageRank(self.graph, PRankH, C, Eps, MaxIter)
for item in PRankH:
ret.append((item, PRankH[item]))
return ret
def eigenvector_centrality(self, Eps=1e-4, MaxIter=100):
'''
Computes eigenvector centrality of all nodes in Graph.
Eigenvector Centrality of a node N is defined recursively as the average of centrality values of N’s neighbors in the network.
:param Eps: Convergence difference.
:param MaxIter: Maximum number of iterations.
'''
snap = self.snap
ret = []
NIdEigenH = snap.TIntFlt64H()
snap.GetEigenVectorCentr(self.graph, NIdEigenH, Eps, MaxIter)
for item in NIdEigenH:
ret.append((item, NIdEigenH[item]))
return ret
def sample_betweenness_centrality(self, quality=1, isDir=False):
'''
Computes (approximate) Node and Edge Betweenness Centrality based on a sample
:param quality: Quality of the approximation. 1.0 gives exact betweenness values.
:param isDir: consider direct or not
'''
snap = self.snap
Nodes = snap.TIntFlt64H()
Edges = snap.TIntPrFlt64H()
snap.GetBetweennessCentr(self.graph, Nodes, Edges, float(quality),
isDir)
node_btwn = []
edge_btwn = []
for node in Nodes:
node_btwn.append(Nodes[node])
for edge in Edges:
edge_btwn.append(Edges[edge])
return (node_btwn, edge_btwn)
import snap
data = snap.LoadEdgeList(snap.PNGraph, "stackoverflow-Java.txt", 0, 1, '\t')
# The number of weakly connected components in the network.
Components = snap.TCnComV()
snap.GetWccs(data, Components)
print("Number of Weakly Connected Components:", Components.Len())
# The number of edges and the number of nodes in the largest weakly connected component
MxWcc = snap.GetMxWcc(data)
print("Number of MxWcc Edges:", MxWcc.GetEdges())
print("Number of MxWcc Nodes:", MxWcc.GetNodes())
# IDs of the top 3 most central nodes in the network by PagePank scores
PRankH = snap.TIntFlt64H()
snap.GetPageRank(data, PRankH)
PRankH.SortByDat(False)
i = 0
itr = PRankH.BegI()
print("The top 3 most central nodes in the network by PagePank scores:")
while i < 3:
print("Node:", itr.GetKey())
itr.Next()
i += 1
print("")
# IDs of the top 3 hubs and top 3 authorities in the network by HITS scores.
NIdHubH = snap.TIntFlt64H()
NIdAuthH = snap.TIntFlt64H()
def all_node_centrality(g, centralityFunc):
centralities = snap.TIntFlt64H()
for node in g.Nodes():
centralities[node.GetId()] = centralityFunc(g, node.GetId())
return centralities
#g_outdeg is a vector of pairs of floats. Each pair is addressed like (Val1,Val2)
outdeg_gt_10 = list(filter(lambda x: x.GetVal2() > 10, g_outdeg))
indeg_gt_10 = list(filter(lambda x: x.GetVal2() > 10, g_indeg))
print(f'Nodes with outdegree > 10: {len(outdeg_gt_10)}')
print(f'Nodes with indegree > 10: {len(indeg_gt_10)}')
#Problem 2
so = snap.LoadEdgeList(snap.PNGraph, "stackoverflow-Java.txt")
#2.1
so_wcc = snap.TCnComV()
snap.GetWccs(so, so_wcc)
print(f'# of connected components: {len(so_wcc)}')
#2.2
so_mx_wcc = snap.GetMxWcc(so)
snap.PrintInfo(so_mx_wcc, "Largest connected component of StackOverflow-Java")
#2.3
so_pr = snap.TIntFlt64H()
snap.GetPageRank(so, so_pr)
so_pr.SortByDat(False) #Ascending=False
#The code below might be a naive way to do it
#Mb try GetKeyV() for hashtable types then just grab top 3 elements
so_pr_ordered = []
so_pr_iter = so_pr.BegI()
while not so_pr_iter.IsEnd():
so_pr_ordered.append((
so_pr_iter.GetKey(),
so_pr_iter.GetDat(),
))
so_pr_iter.Next()
print("Top 3 nodes by PageRank (nodeId,PageRank):")
for kv_pair in so_pr_ordered[0:3]:
print(kv_pair)
import snap
Graph = snap.GenRndGnm(snap.PNGraph, 100, 1000)
PRankH = snap.TIntFlt64H()
snap.GetPageRank(Graph, PRankH)
for item in PRankH:
print item, PRankH[item]
Graph = snap.GenRndGnm(snap.PUNGraph, 5000000, 50000000)
PRankH = snap.TIntFlt64H()
snap.GetPageRank(Graph, PRankH)
for item in PRankH:
print item, PRankH[item]
Graph = snap.GenRndGnm(snap.PNEANet, 100, 1000)
PRankH = snap.TIntFlt64H()
snap.GetPageRank(Graph, PRankH)
for item in PRankH:
print item, PRankH[item]
import snap
Graph = snap.GenRndGnm(snap.PNGraph, 100, 1000)
Nodes = snap.TIntFlt64H()
Edges = snap.TIntPrFlt64H()
snap.GetBetweennessCentr(Graph, Nodes, Edges, 1.0)
for node in Nodes:
print("node: %d centrality: %f" % (node, Nodes[node]))
for edge in Edges:
print("edge: (%d, %d) centrality: %f" %
(edge.GetVal1(), edge.GetVal2(), Edges[edge]))
UGraph = snap.GenRndGnm(snap.PUNGraph, 100, 1000)
Nodes = snap.TIntFlt64H()
Edges = snap.TIntPrFlt64H()
snap.GetBetweennessCentr(UGraph, Nodes, Edges, 1.0)
for node in Nodes:
print("node: %d centrality: %f" % (node, Nodes[node]))
for edge in Edges:
print("edge: (%d, %d) centrality: %f" %
(edge.GetVal1(), edge.GetVal2(), Edges[edge]))
Network = snap.GenRndGnm(snap.PNEANet, 100, 1000)
Nodes = snap.TIntFlt64H()
Edges = snap.TIntPrFlt64H()
snap.GetBetweennessCentr(Network, Nodes, Edges, 1.0)
for node in Nodes:
print("node: %d centrality: %f" % (node, Nodes[node]))
for edge in Edges:
print("edge: (%d, %d) centrality: %f" %
(edge.GetVal1(), edge.GetVal2(), Edges[edge]))