def main():
# Parameters for Stochastic block model graph
# Todal of 1000 nodes
node_num = 1000
# Test with two communities
community_num = 2
# At each iteration migrate 10 nodes from one community to the another
node_change_num = 10
# Length of total time steps the graph will dynamically change
length = 7
# output directory for result
outdir = './output'
intr = './intermediate'
if not os.path.exists(outdir):
os.mkdir(outdir)
if not os.path.exists(intr):
os.mkdir(intr)
testDataType = 'sbm_cd'
# Generate the dynamic graph
dynamic_sbm_series = list(
sbm.get_community_diminish_series_v2(
node_num,
community_num,
length,
1, # comminity ID to perturb
node_change_num))
graphs = [g[0] for g in dynamic_sbm_series]
# parameters for the dynamic embedding
# dimension of the embedding
dim_emb = 8
lookback = 2
# TIMERS
datafile = dataprep_util.prep_input_TIMERS(graphs, length, testDataType)
embedding = TIMERS(K=dim_emb,
Theta=0.5,
datafile=datafile,
length=length,
nodemigration=node_change_num,
resultdir=outdir,
datatype=testDataType)
if not os.path.exists(outdir):
os.mkdir(outdir)
outdir_tmp = outdir + '/sbm_cd'
if not os.path.exists(outdir_tmp):
os.mkdir(outdir_tmp)
if not os.path.exists(outdir_tmp + '/incrementalSVD'):
os.mkdir(outdir_tmp + '/incrementalSVD')
if not os.path.exists(outdir_tmp + '/rerunSVD'):
os.mkdir(outdir_tmp + '/rerunSVD')
if not os.path.exists(outdir_tmp + '/optimalSVD'):
os.mkdir(outdir_tmp + '/optimalSVD')
t1 = time()
embedding.learn_embedding()
embedding.get_embedding(outdir_tmp, 'optimalSVD')
print(embedding._method_name + ':\n\tTraining time: %f' % (time() - t1))
embedding.plotresults(dynamic_sbm_series)
args = parser.parse_args()
dim_emb = args.embeddimension
length = args.timelength
theta = args.theta
sample = args.samples
if args.testDataType == 'sbm_cd':
node_num = 100
community_num = 2
node_change_num = args.nodemigration
dynamic_sbm_series = dynamic_SBM_graph.get_community_diminish_series_v2(
node_num, community_num, length, 1, node_change_num)
graphs = [g[0] for g in dynamic_sbm_series]
datafile = dataprep_util.prep_input_TIMERS(graphs, length,
args.testDataType)
embedding = TIMERS(K=dim_emb,
Theta=theta,
datafile=datafile,
length=length,
nodemigration=args.nodemigration,
resultdir=args.resultdir,
datatype=args.testDataType)
outdir_tmp = './output'
if not os.path.exists(outdir_tmp):
os.mkdir(outdir_tmp)
outdir_tmp = outdir_tmp + '/sbm_cd'
if not os.path.exists(outdir_tmp):
os.mkdir(outdir_tmp)
if not os.path.exists(outdir_tmp + '/incrementalSVD'):
modelfile = ['./intermediate/enc_modelsbm.json',
'./intermediate/dec_modelsbm.json'],
weightfile = ['./intermediate/enc_weightssbm.hdf5',
'./intermediate/dec_weightssbm.hdf5'])
embs = []
t1 = time()
#ae static
for temp_var in range(length):
emb, _= embedding.learn_embeddings(graphs[temp_var])
embs.append(emb)
print (embedding._method_name+':\n\tTraining time: %f' % (time() - t1))
viz.plot_static_sbm_embedding(embs[-4:], dynamic_sbm_series[-4:])
#TIMERS
datafile = dataprep_util.prep_input_TIMERS(graphs, length, testDataType)
embedding = TIMERS(K = dim_emb,
Theta = 0.5,
datafile = datafile,
length = length,
nodemigration = node_change_num,
resultdir = outdir,
datatype = testDataType)
if not os.path.exists(outdir):
os.mkdir(outdir)
outdir_tmp=outdir+'/sbm_cd'
if not os.path.exists(outdir_tmp):
os.mkdir(outdir_tmp)
if not os.path.exists(outdir_tmp+'/incremental'):
os.mkdir(outdir_tmp+'/incrementalSVD')
if not os.path.exists(outdir_tmp+'/rerunSVD'):