def load_genre_graphs(graph):
if graph == "rock":
filename = '../data/genre_graphs/rock_graph/rock_graph.txt'
dict = chords_dict_rock
else:
filename = '../data/genre_graphs/jazz_graph/jazz_graph.txt'
dict = chords_dict_jazz
G_Multi = snap.LoadEdgeList(snap.PNEANet, filename, 0, 1)
G_Directed = snap.LoadEdgeList(snap.PNGraph, filename, 0, 1)
G_Undirected = snap.LoadEdgeList(snap.PUNGraph, filename, 0, 1)
#return G_Multi, G_Directed, G_Undirected, dict
return snap.GetKCore(G_Multi, 3), snap.GetKCore(G_Directed, 3), snap.GetKCore(G_Undirected, 3), dict
def main(genre):
G_Multi, G_Directed, G_Undirected, dict = load_genre_graphs(genre)
# clean up graph
print(G_Undirected.GetNodes())
G_Undirected = snap.GetKCore(G_Undirected, 3)
print(G_Undirected.GetNodes())
NIdEigenH = snap.TIntFltH()
snap.GetEigenVectorCentr(G_Undirected, NIdEigenH)
centralities = []
for item in NIdEigenH:
centralities.append((dict[item], NIdEigenH[item]))
centralities.sort(reverse=True, key=lambda l: l[1])
for centrality in centralities:
print centrality
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())
# 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())
#G6 = snap.GenForestFire(100000, 0.25, 0.25) G6 = snap.GenRndGnm(snap.PNGraph, 10000, 5000) print "type(G6) %s" % (type(G6)) print "G6 nodes %d, edges %d" % (G6.GetNodes(), G6.GetEdges()) G7 = snap.ConvertGraph(snap.PUNGraph, G6) print "type(G7) %s" % (type(G7)) print "G7 nodes %d, edges %d" % (G7.GetNodes(), G7.GetEdges()) WccG6 = snap.GetMxWcc(G6) print "type(WccG6) %s" % (type(WccG6)) print "WccG6 nodes %d, edges %d" % (WccG6.GetNodes(), WccG6.GetEdges()) G8 = snap.GenForestFire(1000, 0.35, 0.35) print "type(G8) %s" % (type(G8)) print "G8 nodes %d, edges %d" % (G8.GetNodes(), G8.GetEdges()) SubG8 = snap.GetSubGraph(G8, snap.TIntV.GetV(0,1,2,3,4)) print "type(SubG8) %s" % (type(SubG8)) print "SubG8 nodes %d, edges %d" % (SubG8.GetNodes(), SubG8.GetEdges()) Core3G8 = snap.GetKCore(G8, 3) print "type(Core3G8) %s" % (type(Core3G8)) print "CoreG8 nodes %d, edges %d" % (Core3G8.GetNodes(), Core3G8.GetEdges()) snap.DelDegKNodes(G8, 3, 2) print "type(G8) %s" % (type(G8)) print "G8 nodes %d, edges %d" % (G8.GetNodes(), G8.GetEdges())
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)
SccG6 = snap.GetMxScc(G6)
print("type(SccG6) %s" % (type(SccG6)))
print("SccG6 nodes %d, edges %d" % (SccG6.GetNodes(), SccG6.GetEdges()))
SccG7 = snap.GetMxScc(G7)
print("type(SccG7) %s" % (type(SccG7)))
print("SccG7 nodes %d, edges %d" % (SccG7.GetNodes(), SccG7.GetEdges()))
SubG6 = snap.GetSubGraph(G6, snap.TIntV.GetV(0, 1, 2, 3, 4))
print("type(SubG6) %s" % (type(SubG6)))
print("SubG6 nodes %d, edges %d" % (SubG6.GetNodes(), SubG6.GetEdges()))
for EI in SubG6.Edges():
print("edge (%d, %d)" % (EI.GetSrcNId(), EI.GetDstNId()))
Core3G6 = snap.GetKCore(G6, 3)
print("type(Core3G6) %s" % (type(Core3G6)))
print("CoreG6 nodes %d, edges %d" % (Core3G6.GetNodes(), Core3G6.GetEdges()))
G8 = snap.GenRndGnm(snap.PNGraph, 1000, 100000)
print("type(G8) %s" % (type(G8)))
print("G8 nodes %d, edges %d" % (G8.GetNodes(), G8.GetEdges()))
WccG8 = snap.GetMxWcc(G8)
print("type(WccG8) %s" % (type(WccG8)))
print("WccG8 nodes %d, edges %d" % (WccG8.GetNodes(), WccG8.GetEdges()))
WccG7 = snap.GetMxWcc(G7)
print("type(WccG7) %s" % (type(WccG7)))
print("WccG7 nodes %d, edges %d" % (WccG7.GetNodes(), WccG7.GetEdges()))
def get_k_core(graph, K=5):
KCore = snap.GetKCore(graph, K)
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()
print "Clustering Co-efficient of the network =", snap.GetClustCf(G1)
# get a subgraph induced on nodes {0,1,2,3,4,5}
SubG = snap.GetSubGraph(G1, snap.TIntV.GetV(2, 4, 6, 8, 10, 12, 14, 16, 20))
snap.DrawGViz(SubG, snap.gvlDot, "recosub.png", "Partial Network Diagram",
True, snap.TIntStrH())
# get 3-core of G
# get largest weakly connected component
WccG = snap.GetMxWcc(G1)
snap.DrawGViz(WccG, snap.gvlDot, "weakconn.png",
"largest weakly connected component", True, snap.TIntStrH())
Core3 = snap.GetKCore(SubG, 3)
snap.DrawGViz(Core3, snap.gvlDot, "recocore.png", "Partial Network Diagram",
True, snap.TIntStrH())
FILE_LOG_NAME = 'LOG_File_' + (FILE_NAME[-1]) + (KCORE_VALUES) + ('.log')
LOG_FILE = open(FILE_LOG_NAME, 'w')
TEMP_INFO = 'Starting Community Detection Section on FILE [ ' + (
FILE_PATH) + ' ] ' + 'with k_core value ' + (KCORE_VALUES)
LOG_FILE.write(TEMP_INFO)
LOG_FILE.write('\n')
G = snap.LoadEdgeList(snap.PUNGraph, FILE_PATH, 0, 1)
LOG_FILE.write('Transaction: Parse External File Successful. \t')
START_TIME = time.time()
LOG_FILE.write('Finish Time: %f' % START_TIME)
LOG_FILE.write('\n')
#print G
NodeV_G = create_node_vector(G)
Kcore = snap.GetKCore(G, int(KCORE_VALUES))
NodeV_Kcore = create_node_vector(Kcore)
#print NodeV_Kcore.Len()
LOG_FILE.write('Transaction: k-core Search Successful. \t')
LOG_FILE.write('Finish Time: %f' % time.time())
LOG_FILE.write('\n')
#print NodeV_Kcore
NodeV_Diff = get_diff_from_kcore(NodeV_G, NodeV_Kcore)
SortH = sort_by_neighbor(NodeV_Diff, Kcore, G)
Partition_KcoreH = community_partition(Kcore)
LOG_FILE.write('Transaction: Partition on K-core Successful. \t')
LOG_FILE.write('Finish Time: %f' % time.time())
LOG_FILE.write('\n')
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
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()
