def detectCommunities(G, algo=None, inspect=True):
""" Perform high-performance community detection on the graph.
:param G the graph
:param algorithm community detection algorithm instance
:return communities (as type Partition)
"""
if algo is None:
algo = PLM(G, refine=False)
t = stopwatch.Timer()
algo.run()
zeta = algo.getPartition()
t.stop()
print("{0} detected communities in {1} [s]".format(algo.toString(), t.elapsed))
if inspect:
print ("solution properties:")
inspectCommunities(zeta, G)
return zeta
def mesoscopicResponseFunction(G, samples=100):
"""
"""
raise NotImplementedError("work in progress")
m = G.numberOfEdges()
gammaRangeLow = [math.e**x for x in range(-10, 0)]
gammaRangeHigh = [math.e**x for x in range(0, math.ceil(math.log(2*m)))]
gammaRange = gammaRangeLow + gammaRangeHigh
print(gammaRange)
nCom = []
for gamma in gammaRange:
communityDetector = PLM(G, gamma=gamma)
communityDetector.run()
communities = communityDetector.getPartition()
nCom.append(communities.numberOfSubsets())
return (gammaRange, nCom)
def mesoscopicResponseFunction(G, samples=100):
"""
"""
raise NotImplementedError("work in progress")
m = G.numberOfEdges()
gammaRangeLow = [math.e**x for x in range(-10, 0)]
gammaRangeHigh = [math.e**x for x in range(0, math.ceil(math.log(2*m)))]
gammaRange = gammaRangeLow + gammaRangeHigh
print(gammaRange)
nCom = []
for gamma in gammaRange:
communityDetector = PLM(G, gamma=gamma)
communityDetector.run()
communities = communityDetector.getPartition()
nCom.append(communities.numberOfSubsets())
return (gammaRange, nCom)
def detectCommunities(G, algo=None, inspect=True):
""" Perform high-performance community detection on the graph.
:param G the graph
:param algorithm community detection algorithm instance
:return communities (as type Partition)
"""
if algo is None:
algo = PLM(G, refine=False)
t = stopwatch.Timer()
algo.run()
zeta = algo.getPartition()
t.stop()
print("{0} detected communities in {1} [s]".format(algo.toString(), t.elapsed))
if inspect:
print ("solution properties:")
inspectCommunities(zeta, G)
return zeta
graphID = 0
for graphName in Gset:
print("graphName ist ", graphName)
graphFile = graphDir + graphName + '.graph'
print("graphFile ist ", graphFile)
start = time.time()
G = readGraph(graphFile, Format.METIS)
finish = time.time()
print("Einlesen von ", graphFile, " dauerte ", finish - start, " Sekunden")
anzPLM3 = 0
anzPLM4 = 0
for seed in range(1, nrSeeds + 1):
#print("seed ist ", seed)
start = time.time()
algo3 = PLM(G, refine=False)
partition3 = detectCommunities(G, algo3, True)
partition3.compact()
finish = time.time()
print("Ausrechnen von partition3 dauerte ", finish - start,
" Sekunden")
start = time.time()
#algo4 = PLM(G, refine=True, 100)
algo4 = PLM(G, refine=True)
partition4 = detectCommunities(G, algo4, True)
partition4.compact()
finish = time.time()
print("Ausrechnen von partition4 dauerte ", finish - start,
" Sekunden")
anzPLM3 = anzPLM3 + partition3.upperBound()
anzPLM4 = anzPLM4 + partition4.upperBound()