def RankEigvecDist(graph):
"""
Calculates the degree - clustering coefficient distribution of a given graph. The graph must be in snap format.
...
Parameters
----------
graph : an instance of SNAP.TUNGraph()/SNAP.TNGraph() for undirected/directed graph
The graph for which the degree distribution is to be calculated
Returns
-------
eigvec_dist : 2D numpy array with shape (2, :)
The calculated eigenvector distribution for graph. Here, the eigenvector refers to the principal eigenvector, which should be positive definite due to Perrin-Frobenius theorem.
eigvec_dist[0, :] : rank of the eigenvector
eigvec_dist[1, :] : value of the element of the first eigenvector, sorted in the order of descending value.
"""
EigVec = snap.TFltV()
snap.GetEigVec(graph,EigVec)
eigvec = np.abs(np.array(EigVec))
N = len(eigvec)
eigvec.reshape((1, N))
rank = np.arange(N).reshape((1, N))
eigvec = eigvec[np.argsort(-eigvec)]
eigvec_dist = np.vstack([rank, eigvec])
return eigvec_dist
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:
print "degree %d: count %d" % (p.GetVal1(), p.GetVal2())
# generate a Preferential Attachment graph on 100 nodes and out-degree of 3
G10 = snap.GenPrefAttach(100, 3)
print "G10: Nodes %d, Edges %d" % (G10.GetNodes(), G10.GetEdges())
# define a vector of floats and get first eigenvector of graph adjacency matrix
EigV = snap.TFltV()
snap.GetEigVec(G10, EigV)
nr = 0
for f in EigV:
nr += 1
print "%d: %.6f" % (nr, f)
# get an approximation of graph diameter
diam = snap.GetBfsFullDiam(G10, 10)
print "diam", diam
# count the number of triads:
triads = snap.GetTriads(G10)
print "triads", triads
# get the clustering coefficient
cf = snap.GetClustCf(G10)
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)
import snap
G1 = snap.TNGraph.New()
G1.AddNode(1)
G1.AddNode(5)
G1.AddNode(32)
G1.AddEdge(1, 1)
G1.AddEdge(1, 5)
G1.AddEdge(5, 1)
G1.AddEdge(5, 32)
for EI in G1.Edges():
print("edge (%d, %d)" % (EI.GetSrcNId(), EI.GetDstNId()))
# get first eigenvector of graph adjacency matrix
EigV = snap.TFltV()
snap.GetEigVec(G1, EigV)
for item in ComponentDist:
print "%d nodes with out-degree %d" % (item.GetVal2(), item.GetVal1())
xval.append(item.GetVal1())
yval.append(item.GetVal2())
bins = np.arange(len(yval))
plt.hist(yval, xval, alpha=0.5, label='Nodes with Out degree')
plt.title('Distribution of Out degree by Nodes')
plt.xlabel('Out degree')
plt.ylabel('Number of Nodes')
plt.xticks(bins, rotation=90)
plt.show()
# vector of floats
EigV = snap.TFltV()
# get first eigenvector of graph adjacency matrix
snap.GetEigVec(snap.ConvertGraph(snap.PUNGraph, G1), EigV)
print "Leading Eigen vector of adjacency graph-"
for Val in EigV:
print Val
# get diameter of G8
print "Diameter of the network =", snap.GetBfsFullDiam(G1, 100)
TriadV = snap.TIntTrV()
# count the number of triads in G8, get the clustering coefficient of G8
print "Number of Triads in the network = ",
snap.GetTriads(G1, TriadV)
for triple in TriadV:
print "For Node Id: ", triple.Val1(
), ",Number of open Triads are: ", triple.Val2(
), ", Number of closed Triads are: ", triple.Val3()
# 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)
# 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)
# %%
