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
# dynAERNN
embedding = DynAERNN(d=dim_emb,
beta=5,
n_prev_graphs=lookback,
nu1=1e-6,
nu2=1e-6,
n_aeunits=[500, 300],
n_lstmunits=[500, dim_emb],
rho=0.3,
n_iter=2,
xeta=1e-3,
n_batch=100,
modelfile=['./intermediate/enc_model_dynAERNN.json',
'./intermediate/dec_model_dynAERNN.json'],
weightfile=['./intermediate/enc_weights_dynAERNN.hdf5',
'./intermediate/dec_weights_dynAERNN.hdf5'],
savefilesuffix="testing")
embs = []
t1 = time()
for temp_var in range(lookback + 1, length + 1):
emb, _ = embedding.learn_embeddings(graphs[:temp_var])
embs.append(emb)
print(embedding._method_name + ':\n\tTraining time: %f' % (time() - t1))
plt.figure()
plt.clf()
plot_dynamic_sbm_embedding.plot_dynamic_sbm_embedding_v2(embs[-5:-1], dynamic_sbm_series[-5:])
plt.show()
def plotresults(self, dynamic_sbm_series):
"""Function to plot the results"""
plt.figure()
plt.clf()
plot_dynamic_sbm_embedding.plot_dynamic_sbm_embedding_v2(self._X[-5:-1], dynamic_sbm_series[-5:])
resultdir = self._resultdir + '/' + self._datatype
if not os.path.exists(resultdir):
os.mkdir(resultdir)
resultdir = resultdir + '/' + self._method
if not os.path.exists(resultdir):
os.mkdir(resultdir)
# plt.savefig('./'+resultdir+'/V_'+self._method+'_nm'+str(self._nodemigration)+'_l'+str(self._length)+'_theta'+str(theta)+'_emb'+str(self._K*2)+'.pdf',bbox_inches='tight',dpi=600)
plt.show()
xeta = 1e-4,
n_batch = 100,
modelfile = ['./intermediate/enc_model_dynAE.json',
'./intermediate/dec_model_dynAE.json'],
weightfile = ['./intermediate/enc_weights_dynAE.hdf5',
'./intermediate/dec_weights_dynAE.hdf5'],
savefilesuffix = "testing" )
embs = []
t1 = time()
for temp_var in range(lookback+1, length+1):
emb, _ = embedding.learn_embeddings(graphs[:temp_var])
embs.append(emb)
print (embedding._method_name+':\n\tTraining time: %f' % (time() - t1))
plt.figure()
plt.clf()
plot_dynamic_sbm_embedding.plot_dynamic_sbm_embedding_v2(embs[-5:-1], dynamic_sbm_series[-5:])
plt.show()
#dynRNN
embedding= DynRNN(d = dim_emb,
beta = 5,
n_prev_graphs = lookback,
nu1 = 1e-6,
nu2 = 1e-6,
n_enc_units = [500,300],
n_dec_units = [500,300],
rho = 0.3,
n_iter = 250,
xeta = 1e-3,
n_batch = 100,
modelfile = ['./intermediate/enc_model_dynRNN.json',
def plot_dynam_graph(title, graph_list):
#dynAERNN
graphs = []
for graph in graph_list:
if not graph == None:
graphs.append(graph)
dynamic_sbm_series = graphs
outdir = os.path.exists(os.path.dirname(__file__)+"/out")
testDataType = 'sbm_cd'
length = len(graph_list)
dim_emb = 128
lookback = 2
embedding = DynAERNN(d = dim_emb,
beta = 5,
n_prev_graphs = lookback,
nu1 = 1e-6,
nu2 = 1e-6,
n_aeunits = [500, 300],
n_lstmunits = [500,dim_emb],
rho = 0.3,
n_iter = 250,
xeta = 1e-3,
n_batch = 100,
modelfile = None, #['./intermediate/enc_model_dynAERNN.json',
#'./intermediate/dec_model_dynAERNN.json'],
weightfile = None, #['./intermediate/enc_weights_dynAERNN.hdf5',
#'./intermediate/dec_weights_dynAERNN.hdf5'],
savefilesuffix = None) #"testing")
embs = []
t1 = time()
for temp_var in range(lookback+1, length+1):
emb, _ = embedding.learn_embeddings(graphs[:temp_var])
embs.append(emb)
print (embedding._method_name+':\n\tTraining time: %f' % (time() - t1))
plt.figure()
plt.clf()
plot_dynamic_sbm_embedding.plot_dynamic_sbm_embedding_v2(embs[-5:-1], dynamic_sbm_series[-5:])
plt.show()
# #dynamicTriad
datafile = dataprep_util.prep_input_dynTriad(graphs, length, testDataType)
embedding= dynamicTriad(niters = 20,
starttime = 0,
datafile = datafile,
batchsize = 1000,
nsteps = length,
embdim = dim_emb,
stepsize = 1,
stepstride = 1,
outdir = outdir,
cachefn = '/tmp/'+ testDataType,
lr = 0.1,
beta = [0.1,0.1],
negdup = 1,
datasetmod = 'core.dataset.adjlist',
trainmod = 'dynamicgem.dynamictriad.core.algorithm.dynamic_triad',
pretrain_size = length,
sampling_args = {},
validation = 'link_reconstruction',
datatype = testDataType,
scale = 1,
classifier = 'lr',
debug = False,
test = 'link_predict',
repeat = 1,
resultdir = outdir,
testDataType = testDataType,
clname = 'lr')
#node_num = node_num )
t1 = time()
embedding.learn_embedding()
print (embedding._method_name+':\n\tTraining time: %f' % (time() - t1))
embedding.get_embedding()
embedding.plotresults(dynamic_sbm_series)
