# get largest weakly connected component
WccG = snap.GetMxWcc(G6)
# generate a network using Forest Fire model
G8 = snap.GenForestFire(1000, 0.35, 0.35)
print "G8: Nodes %d, Edges %d" % (G8.GetNodes(), G8.GetEdges())
# get a subgraph induced on nodes {0,1,2,3,4}
SubG = snap.GetSubGraph(G8, snap.TIntV.GetV(0, 1, 2, 3, 4))
# get 3-core of G8
Core3 = snap.GetKCore(G8, 3)
print "Core3: Nodes %d, Edges %d" % (Core3.GetNodes(), Core3.GetEdges())
# delete nodes of out degree 3 and in degree 2
snap.DelDegKNodes(G8, 3, 2)
# create a directed random graph on 10k nodes and 1k edges
G9 = snap.GenRndGnm(snap.PNGraph, 10000, 1000)
print "G9: Nodes %d, Edges %d" % (G9.GetNodes(), G9.GetEdges())
# define a vector of pairs of integers (size, count) and
# get a distribution of connected components (component size, count)
CntV = snap.TIntPrV()
snap.GetWccSzCnt(G9, CntV)
for p in CntV:
print "size %d: count %d" % (p.GetVal1(), p.GetVal2())
# get degree distribution pairs (out-degree, count):
snap.GetOutDegCnt(G9, CntV)
for p in CntV:
def intro():
# create a graph PNGraph
G1 = snap.TNGraph.New()
G1.AddNode(1)
G1.AddNode(5)
G1.AddNode(32)
G1.AddEdge(1, 5)
G1.AddEdge(5, 1)
G1.AddEdge(5, 32)
print("G1: Nodes %d, Edges %d" % (G1.GetNodes(), G1.GetEdges()))
# create a directed random graph on 100 nodes and 1k edges
G2 = snap.GenRndGnm(snap.PNGraph, 100, 1000)
print("G2: Nodes %d, Edges %d" % (G2.GetNodes(), G2.GetEdges()))
# traverse the nodes
for NI in G2.Nodes():
print("node id %d with out-degree %d and in-degree %d" %
(NI.GetId(), NI.GetOutDeg(), NI.GetInDeg()))
# traverse the edges
for EI in G2.Edges():
print("edge (%d, %d)" % (EI.GetSrcNId(), EI.GetDstNId()))
# traverse the edges by nodes
for NI in G2.Nodes():
for Id in NI.GetOutEdges():
print("edge (%d %d)" % (NI.GetId(), Id))
# generate a network using Forest Fire model
G3 = snap.GenForestFire(1000, 0.35, 0.35)
print("G3: Nodes %d, Edges %d" % (G3.GetNodes(), G3.GetEdges()))
# save and load binary
FOut = snap.TFOut("test.graph")
G3.Save(FOut)
FOut.Flush()
FIn = snap.TFIn("test.graph")
G4 = snap.TNGraph.Load(FIn)
print("G4: Nodes %d, Edges %d" % (G4.GetNodes(), G4.GetEdges()))
# save and load from a text file
snap.SaveEdgeList(G4, "test.txt", "Save as tab-separated list of edges")
G5 = snap.LoadEdgeList(snap.PNGraph, "test.txt", 0, 1)
print("G5: Nodes %d, Edges %d" % (G5.GetNodes(), G5.GetEdges()))
# generate a network using Forest Fire model
G6 = snap.GenForestFire(1000, 0.35, 0.35)
print("G6: Nodes %d, Edges %d" % (G6.GetNodes(), G6.GetEdges()))
# convert to undirected graph
G7 = snap.ConvertGraph(snap.PUNGraph, G6)
print("G7: Nodes %d, Edges %d" % (G7.GetNodes(), G7.GetEdges()))
# get largest weakly connected component of G
WccG = snap.GetMxWcc(G6)
# get a subgraph induced on nodes {0,1,2,3,4,5}
SubG = snap.GetSubGraph(G6, snap.TIntV.GetV(0, 1, 2, 3, 4))
# get 3-core of G
Core3 = snap.GetKCore(G6, 3)
# delete nodes of out degree 10 and in degree 5
snap.DelDegKNodes(G6, 10, 5)
print("G6a: Nodes %d, Edges %d" % (G6.GetNodes(), G6.GetEdges()))
# generate a Preferential Attachment graph on 1000 nodes and node out degree of 3
G8 = snap.GenPrefAttach(1000, 3)
print("G8: Nodes %d, Edges %d" % (G8.GetNodes(), G8.GetEdges()))
# vector of pairs of integers (size, count)
CntV = snap.TIntPrV()
# get distribution of connected components (component size, count)
snap.GetWccSzCnt(G8, CntV)
# get degree distribution pairs (degree, count)
snap.GetOutDegCnt(G8, CntV)
# vector of floats
EigV = snap.TFltV()
# get first eigenvector of graph adjacency matrix
snap.GetEigVec(G8, EigV)
# get diameter of G8
snap.GetBfsFullDiam(G8, 100)
# count the number of triads in G8, get the clustering coefficient of G8
snap.GetTriads(G8)
snap.GetClustCf(G8)
##########################################################################
f_in = snap.TFIn(FOLDED_POSTID_GRAPH_PATH)
post_graph = snap.TUNGraph.Load(f_in)
print "nodes", post_graph.GetNodes()
print "edges", post_graph.GetEdges()
COMMUNITIES_PATH = path.join(BASE_PATH,
'postid-communities-with-postbodies.txt')
COMMUNITIES_VEC_PATH = path.join(BASE_PATH, 'postid-communities.vector')
# remove degree-1 nodes
assert snap.CntSelfEdges(post_graph) == 0
snap.DelDegKNodes(post_graph, 1, 1)
comm_vec = snap.TCnComV()
modularity = snap.CommunityCNM(post_graph, comm_vec)
f_out = snap.TFOut(COMMUNITIES_VEC_PATH)
comm_vec.Save(f_out)
f_out.Flush()
f_in = snap.TFIn(COMMUNITIES_VEC_PATH)
comm_vec = snap.TCnComV()
comm_vec.Load(f_in)
print "communities", len(comm_vec)
pickle_file = open(POSTID_PICKLE, 'rb')
def aggregate(userList, N_CLAIM=5, inDEG=0):
G = snap.TUNGraph.New()
# keep the following codes for reading facebook data
#for userId in userList:
# filename = str(userId) + '.egonet'
# read_nodeadjlist(egonetFolderName + filename, G, MAX_DEGREE = 5)
claimantList = []
for i in xrange(N_CLAIM):
temp = snap.GenStar(snap.PUNGraph, random.randrange(3, 8), False)
countID = G.GetNodes()
for NI in temp.Nodes():
G.AddNode(countID + NI.GetId())
if random.random() < 0.5:
claimantList.append(countID + NI.GetId())
for E in temp.Edges():
G.AddEdge(E.GetSrcNId()+countID, E.GetDstNId()+countID)
# removing the NODES with low degrees
snap.DelDegKNodes(G, inDEG, inDEG)
print '\nAfter remove Nodes with In-Degree = %d and out-Degree = %d ' % (inDEG, inDEG)
print 'Graph has %d Nodes and %d Edges\n' % (G.GetNodes(), G.GetEdges())
# Multi-layer network recovery
plot = plotting(G, snap.gvlNeato, True)
plot.hirachical()
# group split
pos1 = 0
pos2 = 0
group = [0.5, 0.3, 0.2, 0.1] # specify the portion of data to split
neighbor = []
N = len(plot.community)
# normalizatoin
group = [float(i)/sum(group) for i in group]
for i in xrange(len(group)):
pos2 = pos1 + int(group[i] * N)
temp = []
for g in plot.community[pos1:pos2]:
for NI in G.Nodes():
if snap.GetShortPath(G, NI.GetId(), g) == 1:
temp.append(NI.GetId())
neighbor.append(temp)
pos1 = pos2
# Add extra links
ratio = [0.0, 0.4, 0.8] # the multi-layer effect by adding links layer by layer
in_w = 0.3 # the density of in-group edges
between_w = 0.4 # the density of between-group edges
in_deg = 11
between_deg = 12
for r in xrange(len(ratio)):
# multi-layer loop
pos1 = 0
pos2 = 0
for i in xrange(len(group)):
# sub-group loop
pos2 = pos1 + int(N * group[i])
for node in plot.community[pos1:pos2]:
if random.random() > ratio[r]:
continue
# inter-group
for NI in neighbor[i]:
if NI not in plot.community \
and NI not in claimantList \
and random.random() < in_w:
if G.GetNI(node).GetInDeg() <= in_deg:
G.AddEdge(node, NI)
#intra-group: adjusting the index periodically but leave off the last element for fraud ring
if i == len(group)-2:
k = 0
elif i == len(group)-1:
continue
else:
k = i + 1
for NI in neighbor[k]:
if NI not in plot.community \
and NI not in claimantList \
and random.random() < between_w:
if G.GetNI(node).GetInDeg() <= between_deg:
G.AddEdge(node, NI)
plot.run('aggregate_plot_round={}_{}'.format(r, '.'.join(userList)),
claimantList,
title='USER_ID = {}'.format('.'.join(userList)))
pos1 = pos2
snap.SaveEdgeList(G4, "test.txt", "Save as tab-separated list of edges")
G5 = snap.LoadEdgeList(snap.PNGraph, "test.txt", 0, 1)
# %%
# Manipulate
# generate a network using Forest Fire model
G6 = snap.GenForestFire(1000, 0.35, 0.35)
# convert to undirected graph
G7 = snap.ConvertGraph(snap.PUNGraph, G6)
WccG = snap.GetMxWcc(G6)
# get a subgraph induced on nodes {0,1,2,3,4,5}
SubG = snap.GetSubGraph(G6, snap.TIntV.GetV(0, 1, 2, 3, 4))
# get 3-core of G
Core3 = snap.GetKCore(G6, 3)
# delete nodes of out degree 10 and in degree 5
snap.DelDegKNodes(G6, 10, 5)
# %%
# stats
# generate a Preferential Attachment graph on 1000 nodes and node out degree of 3
G8 = snap.GenPrefAttach(1000, 3)
# vector of pairs of integers (size, count)
CntV = snap.TIntPrV()
# get distribution of connected components (component size, count)
snap.GetWccSzCnt(G8, CntV)
# get degree distribution pairs (degree, count)
snap.GetOutDegCnt(G8, CntV)
# vector of floats
EigV = snap.TFltV()
# get first eigenvector of graph adjacency matrix
snap.GetEigVec(G8, EigV)
import snap
mapping = snap.TStrIntSH()
G0 = snap.LoadEdgeListStr(snap.PNEANet, "dataList.txt", 0, 1, mapping)
snap.SaveEdgeList(G0, "testGraph.txt", "Save as tab-separated list of edges")
snap.PrintInfo(G0)
"""
snap.DelDegKNodes(G0,1,0)
snap.DelDegKNodes(G0,1,0)
snap.DelDegKNodes(G0,1,0)
snap.DelDegKNodes(G0,1,0)
snap.PrintInfo(G0)
DegToCntV = snap.TIntPrV()
snap.GetOutDegCnt(G0, DegToCntV)
for item in DegToCntV:
print "%d nodes with out-degree %d" % (item.GetVal2(), item.GetVal1())
"""
"""
Components = snap.TCnComV()
snap.GetSccs(G0, Components)
for CnCom in Components:
print "Size of component: %d" % CnCom.Len()
"""
"""
DegToCntV = snap.TIntPrV()