def print_average_ccf_and_traid_count(G, GName):
DegToCCfV = snap.TFltPrV()
result = snap.GetClustCfAll(G, DegToCCfV)
print "Average clustering coefficient in {0}: {1:.4f}".format(
GName[:-10], result[0])
print "Number of traids in {0}: {1}".format(GName[:-10], result[1])
def showClusteringCoefficient(network):
DegToCCfV = snap.TFltPrV()
result = snap.GetClustCfAll(network, DegToCCfV)
for item in DegToCCfV:
print("degree: %d, clustering coefficient: %f" % (item.GetVal1(), item.GetVal2()))
print("average clustering coefficient", result[0])
print("closed triads", result[1])
print("open triads", result[2])
def gen_config_model_rewire(graph, iterations=8000, seed=42):
config_graph = graph
clustering_coeffs = []
##########################################################################
nOfNodes = graph.GetNodes()
nOfEdges = graph.GetEdges()
for j in range(iterations):
while True:
edges1 = graph.BegEI()
eId = int(round(np.random.rand() * (nOfEdges - 1)))
for i in range(eId):
edges1.Next()
e1 = edges1
a = e1.GetSrcNId()
b = e1.GetDstNId()
edges2 = graph.BegEI()
e2id = int(round(np.random.rand() * (nOfEdges - 1)))
for i in range(e2id):
edges2.Next()
e2 = edges2
c = e2.GetSrcNId()
d = e2.GetDstNId()
u = a if np.random.rand() > 0.5 else b
v = b if u == a else a
w = c if np.random.rand() > 0.5 else d
x = d if w == c else c
# check if graph is regular
if u == w or v == x or (a == b and c == d) or graph.IsEdge(
u, w) or graph.IsEdge(v, x):
continue
graph.DelEdge(a, b)
graph.DelEdge(c, d)
graph.AddEdge(u, w)
graph.AddEdge(v, x)
break
if graph.GetNodes() != nOfNodes or graph.GetEdges() != nOfEdges:
print "Graph has changed! nodes:", nOfNodes, "-->", graph.GetNodes(
), "edges:", nOfEdges, "-->", graph.GetEdges()
raise RuntimeError
if j % 100 == 0:
clustering_coeffs.append(
snap.GetClustCfAll(graph, snap.TFltPrV())[0])
##########################################################################
return config_graph, clustering_coeffs
def clustCoefDistribution(G):
print "Nodes number: ", G.GetNodes() # 37444
print "Edges number: ", G.GetEdges() # 561119
DegToCCfV = snap.TFltPrV()
snap.GetClustCfAll(G, DegToCCfV)
X = []
Y = []
for item in DegToCCfV:
X.append(item.GetVal1()) # degree
Y.append(item.GetVal2()) # avg. clustering coefficient
plt.plot(X, Y, 'ro', label='Collaboration Network')
plt.ylabel('Average Clustering Coefficient')
plt.xlabel('Number of Neighbors (degree)')
plt.show()
import numpy as np
import snap
import matplotlib.pyplot as plt
import random
payoff_a = 2
payoff_b = 3
thersold = payoff_a / (payoff_a + payoff_b)
nodestatusList = []
nodedegreeList = []
nodeaffectList = []
nodeinitialList = []
G = snap.LoadEdgeList(snap.PUNGraph, "data/soc-Slashdot0902.txt", 0, 1, '\t')
snap.PlotClustCf(G, "project_cluster_coeff",
"Undirected graph - clustering coefficient")
DegToCCfV = snap.TFltPrV()
result = snap.GetClustCfAll(G, DegToCCfV)
for item in DegToCCfV:
print("degree: %d, clustering coefficient: %f" %
(item.GetVal1(), item.GetVal2()))
print("average clustering coefficient", result[0])
clusterfile = open("clustering_list.txt", "w")
NIdCCfH = snap.TIntFltH()
snap.GetNodeClustCf(G, NIdCCfH)
for item in NIdCCfH:
clusterfile.write("%d %d\r\n" % (item, NIdCCfH[item]))
clustering_coef_current_node = 2 * count_edges_between_neigbours / (
current_degree * (current_degree - 1))
list_clusterning_coefs_allnodes.append(clustering_coef_current_node)
return list_clusterning_coefs_allnodes
list_clusterning_coefs_allnodes = clustering_coef(
UGraph, mode_for_end_nodes='put_zeros')
list_clusterning_coefs_allnodes
gcc = np.mean(list_clusterning_coefs_allnodes)
lcc = snap.GetClustCf(UGraph)
#-Compute another Global Clustering Coef - Transitivity
DegToCCfV = snap.TFltPrV()
triads = snap.GetClustCfAll(UGraph, DegToCCfV)
for item in DegToCCfV:
print("degree: %d, clustering coefficient: %f" %
(item.GetVal1(), item.GetVal2()))
print("average clustering coefficient", result[0])
print("closed triads", result[1])
print("open triads", result[2])
closed_triads_num = triads[1]
open_triads_num = triads[2]
transitivity = 3 * closed_triads_num / open_triads_num
print(
"----------------------------------------------------------\n-------------Clustering Coef Result------------------\n----------------------------------------------------------\n Built-in Average of Local Clustering Coefs:",
lcc, "\n Global Clustering Coef(Average all nodes' LocalCC):", gcc,
"\n Global Clustering Coef(Transitivity=3*(#triangles)/(#triplets)):",
def clustering_coefficient(G):
DegToCCfV = snap.TFltPrV()
Result = snap.GetClustCfAll(G, DegToCCfV, -1)
print 'Average Clustering Coefficient:', Result[0]
def average_clustering_coefficient(self):
return sn.GetClustCfAll(self.gUNsn, sn.TFltPrV())[0]
Community = 0
SubGraphVector = snap.TIntV()
d = defaultdict(list)
for Cmty in CmtyV:
Community += 1
print "Community: %d" % Community
for NI in Cmty:
d[Community].append(NI)
NodeIdList.Add(NI)
SubGraph = snap.GetSubGraph(G1, NodeIdList)
diam = snap.GetBfsFullDiam(SubGraph, 100, False)
print "The diametre is %d" % diam
DegToCCfV = snap.TFltPrV()
DegList = list()
result = snap.GetClustCfAll(SubGraph, DegToCCfV)
for item in DegToCCfV:
DegList.append(item.GetVal1())
#print "degree: %d, clustering coefficient: %f" % (item.GetVal1(), item.GetVal2())
print "average clustering coefficient", result[0]
print "closed triads", result[1]
print "open triads", result[2]
NumTriadEdges = snap.GetTriadEdges(SubGraph)
print "The number of TriadEdges is %d" % NumTriadEdges
CountEdges = snap.CntUniqUndirEdges(SubGraph)
print "Directed Graph: Count of undirected edges is %d" % CountEdges
CountNodes = SubGraph.GetNodes()
InternalDensity = CountEdges / (CountNodes * (CountNodes - 1) * 0.5)
print "The Internal Density is %f" % InternalDensity
AvgDeg = 2 * CountEdges / CountNodes
print "The Average Degree is %f" % AvgDeg