def draw(self):
self.read_nodes()
self.Network = self.var_network.get()
self.nPoints = sum(int(i) for i in self.lst_Nodes)
if self.Network == "GenStar":
print "GenStar is the network with points ", self.nPoints
self.graph = snap.GenStar(snap.PNGraph, self.nPoints, True)
if self.Network == "GenRndGnm":
print "GenRndGnm is the network with points ", self.nPoints
self.graph = snap.GenRndGnm(snap.PNGraph, self.nPoints,
self.nPoints)
if self.Network == "GenForestFire":
print "GenForestFire is the network with points ", self.nPoints
self.graph = snap.GenForestFire(self.nPoints, 0.5, 0.5)
if self.Network == "GenFull":
print "GenFull is the network with points ", self.nPoints
self.graph = snap.GenFull(snap.PNGraph, self.nPoints)
if self.Network == "GenCircle":
print "GenCircle is the network with points ", self.nPoints
self.graph = snap.GenCircle(snap.PNGraph, self.nPoints, 10, 10)
self.create_nodes(self.graph)
def gen_ff(args):
"""Generate FF Graph"""
for i in range(args.num_graphs):
fp = np.random.uniform(0, 0.5)
bp = np.random.uniform(0, 0.5)
Graph = snap.GenForestFire(args.num_vertices, fp, bp)
snap.SaveEdgeList(Graph, f'{args.data_loc}/FF/FF_{i}.edges')
print(f"FF Graph {i} Generated and Saved")
def generate_graphs():
for path in GRAPHS:
name = path.split('/')[-1].split('.')[0]
metrics = Metrics(path, True).calculate_basic()
print metrics
# Generate Erdos-Renyi (Random) Graph
# args: type, num_nodes, num_edges
er = snap.GenRndGnm(snap.PNGraph, metrics.num_nodes, metrics.num_edges)
snap.SaveEdgeList(er, "{}_er.elist".format(name))
# Generate Watts-Strogatz (Small World) Graph
# args: num_nodes, node_out_degree (average out degree will be twice this value, rewire_prob)
ws = snap.GenSmallWorld(metrics.num_nodes,
int(metrics.avg_degree) / 2, 0.2)
snap.SaveEdgeList(ws, "{}_ws.elist".format(name))
# Generate Barabasi-Albert model (scale-free with preferential attachment) Graph
# args: (num_nodes, degree of each node desired)
ba = snap.GenPrefAttach(metrics.num_nodes, int(metrics.avg_degree) / 2)
snap.SaveEdgeList(ba, "{}_ba.elist".format(name))
# Generate Forest Fire model Graph
# args: (num_nodes, forward_prob, backward_prob)
if name == "USairport_2010":
ff = snap.GenForestFire(
metrics.num_nodes, 0.3599,
0.3599) # Selected value for US Airports data-set
snap.SaveEdgeList(ff, "{}_ff.elist".format(name))
ff = snap.GenForestFire(int(metrics.num_nodes / 10), 0.3599,
0.3599)
snap.SaveEdgeList(ff, "{}_ffdiv10.elist".format(name))
ff = snap.GenForestFire(metrics.num_nodes * 10, 0.3599, 0.3599)
snap.SaveEdgeList(ff, "{}_ffx10.elist".format(name))
else:
ff = snap.GenForestFire(metrics.num_nodes, 0.3467,
0.3467) # selected
snap.SaveEdgeList(ff, "{}_ff.elist".format(name))
ff = snap.GenForestFire(int(metrics.num_nodes / 10), 0.3467,
0.3467)
snap.SaveEdgeList(ff, "{}_ffdiv10.elist".format(name))
ff = snap.GenForestFire(metrics.num_nodes * 10, 0.3467, 0.3467)
snap.SaveEdgeList(ff, "{}_ffx10.elist".format(name))
def SimpleNetworkGenerator(self, params):
#print params
Rnd = snap.TRnd(1, 0)
G = None
try:
if params[0] == GlobalParameters.NetworkType[0]:
G = snap.GenSmallWorld(int(params[1]), int(params[2]),
float(params[3]), Rnd)
if params[0] == GlobalParameters.NetworkType[1]:
G = snap.GenPrefAttach(int(params[1]), int(params[2]), Rnd)
if params[0] == GlobalParameters.NetworkType[2]:
G = snap.GenForestFire(int(params[1]), float(params[2]),
float(params[3]))
if params[0] == GlobalParameters.NetworkType[3]:
G = snap.GenRndGnm(snap.PUNGraph, int(params[1]),
int(params[2]), False, Rnd)
if params[0] == GlobalParameters.NetworkType[4]:
G = snap.GenCircle(snap.PUNGraph, int(params[1]),
int(params[2]), False)
if params[0] == GlobalParameters.NetworkType[5]:
G = snap.GenFull(snap.PUNGraph, int(params[1]))
return G
except:
return None
## #print EI.GetId(),EI.GetOutDeg()
##
## for j in Graph.Edges():
##
## if j.GetSrcNId() == EI.GetId():
## print i,"edge: (%d, %d)" % (j.GetSrcNId(), j.GetDstNId())
## i = i + 1
##
##
##print "*" * 30
####for EI in Graph.Edges():
#### print i
#### print "edge: (%d, %d)" % (EI.GetSrcNId(), EI.GetDstNId())
#### i = i + 1
##
##UGraph = snap.GenRndGnm(snap.PUNGraph, 3,3)
##i =1
##for EI in UGraph.Edges():
## print i,"edge: (%d, %d)" % (EI.GetSrcNId(), EI.GetDstNId())
## i = i + 1
##
##print "*" * 30
##Network = snap.GenRndGnm(snap.PNEANet, 3,3)
##i=1
##for EI in Network.Edges():
## print i,"edge: (%d, %d)" % (EI.GetSrcNId(), EI.GetDstNId())
## i = i + 1
Graph = snap.GenForestFire(10, 0.5, 0.5)
for EI in Graph.Edges():
print "edge: (%d, %d)" % (EI.GetSrcNId(), EI.GetDstNId())
# 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())
# create a directed random graph on 10k nodes and 5k edges
G6 = snap.GenRndGnm(snap.PNGraph, 10000, 5000)
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
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())
def drawNode(self):
global GNodesItemNo
if self.Network == "GenStar":
print "GenStar is the network with points ",self.nPoints
Graph = snap.GenStar(snap.PNGraph, self.nPoints, True)
if self.Network == "GenRndGnm":
print "GenRndGnm is the network with points ",self.nPoints
Graph = snap.GenRndGnm(snap.PNGraph,self.nPoints, self.nPoints)
if self.Network == "GenForestFire":
print "GenForestFire is the network with points ",self.nPoints
Graph = snap.GenForestFire(self.nPoints, 0.5,0.5)
if self.Network == "GenFull":
print "GenFull is the network with points ",self.nPoints
Graph = snap.GenFull(snap.PNGraph,self.nPoints)
if self.Network == "GenCircle":
print "GenCircle is the network with points ",self.nPoints
Graph = snap.GenCircle(snap.PNGraph,self.nPoints,10,10)
shape = 0
self.dnodes.reverse()
nodeCounter = self.dnodes.pop()
for i in Graph.Nodes():
self.node = Node()
self.node.id = i.GetId()
for EI in Graph.Edges():
if EI.GetSrcNId() == i.GetId():
if EI.GetSrcNId() <> EI.GetDstNId() :
self.node.follower.append(EI.GetDstNId())
if shape ==0:
self.node.shape = "oval"
if shape ==1:
self.node.shape = "triangle"
if shape ==2:
self.node.shape = "rectangle"
if shape ==3:
self.node.shape = "circle"
nodeCounter = nodeCounter - 1
if nodeCounter <=0:
if len(self.dnodes)<> 0:
nodeCounter = self.dnodes.pop()
print "Completed Community: ",shape
shape = shape + 1
self.CoordinateSelection()
self.Radius = 5
## if len(self.node.follower) <> 0:
## self.Radius = len(self.node.follower)
if self.node.shape == "oval":
_Oval = Gcanvas.create_oval(self.node.x,self.node.y,self.node.x+self.Radius,
self.node.y+self.Radius,outline="#ff00ff",fill="white", width=2)
if self.node.shape == "triangle":
traingle = [self.node.x,self.node.y,self.node.x+self.Radius,self.node.y+self.Radius]
_Oval = Gcanvas.create_oval(traingle,outline="#252568",fill="white", width=2)
if self.node.shape == "rectangle":
_Oval = Gcanvas.create_oval(self.node.x,self.node.y,self.node.x+self.Radius,
self.node.y+self.Radius,outline="#474bcc",fill="white", width=2)
if self.node.shape == "circle":
_Oval = Gcanvas.create_oval(self.node.x,self.node.y,self.node.x+self.Radius,
self.node.y+self.Radius,outline="#364949",fill="white", width=2)
self.OvalNo[_Oval]=self.node.id#[self.node.x,self.node.y]
Gcanvas.tag_bind( _Oval, '<ButtonPress-1>', self.__showAttriInfo)
self.itemNo.append(_Oval)
self.pAll.append(self.node)
GNodesItemNo = self.itemNo
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)
# 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))
# %%
# I/O
# generate a network using Forest Fire model
G3 = snap.GenForestFire(1000, 0.35, 0.35)
# save and load binary
FOut = snap.TFOut("test.graph")
G3.Save(FOut)
FOut.Flush()
FIn = snap.TFIn("test.graph")
G4 = snap.TNGraph.Load(FIn)
# 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)
# %%
# Manipulate
# generate a network using Forest Fire model
G6 = snap.GenForestFire(1000, 0.35, 0.35)
# convert to undirected graph
