def binary_community_graph(N, k, maxk, mu):
## OS system is windows
if sys.platform[0] == "w":
args = ["gem/c_exe/benchm.exe"]
fcall = "gem/c_exe/benchm.exe"
else:
args = ["gem/c_exe/benchm"]
fcall = "gem/c_exe/benchm"
args.append("-N %d" % N)
args.append("-k %d" % k)
args.append("-maxk %d" % maxk)
args.append("-mu %f" % mu)
t1 = time()
print(args)
try:
os.system("%s -N %d -k %d -maxk %d -mu %f" % (fcall, N, k, maxk, mu))
# call(args)
except Exception as e:
print('ERROR: %s' % str(e))
print('gem/c_exe/benchm not found. Please compile gf, place benchm in the path and grant executable permission')
t2 = time()
print('\tTime taken to generate random graph: %f sec' % (t2 - t1))
try:
graph = graph_util.loadGraphFromEdgeListTxt('gem/c_exe/network.dat')
node_labels = np.loadtxt('gem/c_exe/community.dat')
except:
graph = graph_util.loadGraphFromEdgeListTxt('network.dat')
node_labels = np.loadtxt('community.dat')
node_labels = node_labels[:, -1].reshape(-1, 1)
enc = OneHotEncoder()
return graph, enc.fit_transform(node_labels)
def learn_embedding(self,
graph=None,
edge_f=None,
is_weighted=False,
no_python=False):
c_flag = True
if not graph and not edge_f:
raise Exception('graph/edge_f needed')
if no_python:
try:
from c_ext import graphFac_ext
except ImportError:
print(
'Could not import C++ module for Graph Factorization. Reverting to python implementation. Please recompile graphFac_ext from graphFac.cpp using bjam'
)
c_flag = False
if c_flag:
if edge_f:
graph = graph_util.loadGraphFromEdgeListTxt(edge_f)
graph_util.saveGraphToEdgeListTxt(graph, 'tempGraph.graph')
is_weighted = True
edge_f = 'tempGraph.graph'
t1 = time()
graphFac_ext.learn_embedding(edge_f, "tempGraphGF.emb", True,
is_weighted, self._d, self._eta,
self._regu, self._max_iter)
self._X = graph_util.loadEmbedding('tempGraphGF.emb')
t2 = time()
return self._X, (t2 - t1)
if not graph:
graph = graph_util.loadGraphFromEdgeListTxt(edge_f)
t1 = time()
self._node_num = graph.number_of_nodes()
self._X = 0.01 * np.random.randn(self._node_num, self._d)
for iter_id in range(self._max_iter):
if not iter_id % self._print_step:
[f1, f2, f] = self._get_f_value(graph)
print('\t\tIter id: %d, Objective: %g, f1: %g, f2: %g' %
(iter_id, f, f1, f2))
for i, j, w in graph.edges(data='weight', default=1):
if j <= i:
continue
term1 = -(w -
np.dot(self._X[i, :], self._X[j, :])) * self._X[j, :]
term2 = self._regu * self._X[i, :]
delPhi = term1 + term2
self._X[i, :] -= self._eta * delPhi
t2 = time()
return self._X, (t2 - t1)
def learn_embedding(self,
graph=None,
edge_f=None,
is_weighted=False,
no_python=False):
if not graph and not edge_f:
raise Exception('graph/edge_f needed')
if edge_f:
graph = graph_util.loadGraphFromEdgeListTxt(edge_f)
graph_util.write_edgelist(graph, 'tempGraph_verse.graph')
try:
os.system(
"python gem/verse-master/python/convert.py tempGraph_verse.graph outgraph_verse.bcsr"
)
except Exception as e:
print(str(e))
args = "gem/verse-master/src/verse -input outgraph_verse.bcsr -output tempGraph_verse.emb -dim " + str(
self._d) + " -alpha " + str(self._alpha) + " -threads " + str(
self._threads) + " -nsamples " + str(self._nsamples)
t1 = time()
try:
os.system(args)
except Exception as e:
print(str(e))
raise Exception(
'./verse not found. Please compile, place verse in the path and grant executable permission'
)
self._X = np.fromfile('tempGraph_verse.emb',
np.float32).reshape(graph.number_of_nodes(),
self._d)
t2 = time()
return self._X, (t2 - t1)
def learn_embedding(self,
graph=None,
edge_f=None,
is_weighted=False,
no_python=False):
if not graph and not edge_f:
raise Exception('graph/edge_f needed')
if not graph:
graph = graph_util.loadGraphFromEdgeListTxt(edge_f)
graph = graph.to_undirected()
t1 = time()
L_sym = nx.normalized_laplacian_matrix(graph)
k = self._d + 1
# We hit this error message https://stackoverflow.com/questions/18436667/python-scipy-sparse-matrix-svd-with-error-arpack-error-3-no-shifts-could-be-app
# so we are increasing ncv accordingly
w, v = lg.eigs(L_sym, k=k, which='SM', ncv=4 * k)
idx = np.argsort(w) # sort eigenvalues
w = w[idx]
v = v[:, idx]
t2 = time()
self._X = v[:, 1:]
p_d_p_t = np.dot(v, np.dot(np.diag(w), v.T))
eig_err = np.linalg.norm(p_d_p_t - L_sym)
# print('Laplacian matrix recon. error (low rank): %f' % eig_err)
return self._X.real, (t2 - t1)
def learn_embedding(self,
graph=None,
edge_f=None,
is_weighted=False,
no_python=False):
args = ["gem/c_exe/node2vec"]
if not graph and not edge_f:
raise Exception('graph/edge_f needed')
if edge_f:
graph = graph_util.loadGraphFromEdgeListTxt(edge_f)
graph_util.saveGraphToEdgeListTxtn2v(graph, 'embs/n2v_tempGraph.graph')
args.append("-i:embs/n2v_tempGraph.graph")
args.append("-o:embs/n2v_tempGraph.emb")
args.append("-d:%d" % self._d)
args.append("-l:%d" % self._walkLength)
args.append("-r:%d" % self._numWalks)
args.append("-k:%d" % self._contextSize)
args.append("-e:%d" % self._max_iter)
args.append("-p:%f" % self._return_p)
args.append("-q:%f" % self._inout_p)
args.append("-v")
args.append("-dr")
args.append("-w")
t1 = time()
try:
call(args)
except Exception as e:
print(str(e))
raise Exception(
'./node2vec not found. Please compile snap, place node2vec in the path and grant executable permission'
)
t2 = time()
self._X = graph_util.loadEmbedding('embs/n2v_tempGraph.emb')
return self._X, (t2 - t1)
def learn_embedding(self,
graph=None,
edge_f=None,
is_weighted=False,
no_python=False):
if not graph and not edge_f:
raise Exception('graph/edge_f needed')
if not graph:
graph = graph_util.loadGraphFromEdgeListTxt(edge_f)
t1 = time()
# A = nx.to_scipy_sparse_matrix(graph)
# I = sp.eye(graph.number_of_nodes())
# M_g = I - self._beta*A
# M_l = self._beta*A
A = nx.to_numpy_matrix(graph)
M_g = np.eye(graph.number_of_nodes()) - self._beta * A
M_l = self._beta * A
S = np.dot(np.linalg.inv(M_g), M_l)
u, s, vt = lg.svds(S, k=self._d // 2)
X1 = np.dot(u, np.diag(np.sqrt(s)))
X2 = np.dot(vt.T, np.diag(np.sqrt(s)))
t2 = time()
self._X = np.concatenate((X1, X2), axis=1)
p_d_p_t = np.dot(u, np.dot(np.diag(s), vt))
eig_err = np.linalg.norm(p_d_p_t - S)
print('SVD error (low rank): %f' % eig_err)
# p_d_p_t = np.dot(self._X, np.dot(w[1:self._d+1, 1:self._d+1], self._X.T))
# eig_err = np.linalg.norm(p_d_p_t - L_sym)
# print 'Laplacian reconstruction error (low rank approx): %f' % eig_err
return self._X, (t2 - t1)
def main(opts):
dataset = opts.dataset
embed_dim = int(opts.dimension)
# File that contains the edges. Format: source target
# Optionally, you can add weights as third column: source target weight
edge_f = 'Data/%s.edgelist' % dataset
# Specify whether the edges are directed
# isDirected = True
print "Loading Dataset"
# Load graph
G = graph_util.loadGraphFromEdgeListTxt(edge_f, directed=False)
#G = G.to_directed()
embedding = LaplacianEigenmaps(d=embed_dim)
print('Num nodes: %d, num edges: %d' %
(G.number_of_nodes(), G.number_of_edges()))
t1 = time()
# Learn embedding - accepts a networkx graph or file with edge list
print "Starting Embedding"
Y, t = embedding.learn_embedding(graph=G,
edge_f=None,
is_weighted=True,
no_python=True)
print(embedding._method_name + ':\n\tTraining time: %f' % (time() - t1))
np_save(writable("Embedding_Results", "jac_" + dataset + str(embed_dim)),
Y)
def learn_embedding(self,
graph=None,
edge_f=None,
is_weighted=False,
no_python=False):
if not graph and not edge_f:
raise Exception('graph/edge_f needed')
if not graph:
graph = graph_util.loadGraphFromEdgeListTxt(edge_f)
graph = graph.to_undirected()
t1 = time()
A = nx.to_scipy_sparse_matrix(graph)
normalize(A, norm='l1', axis=1, copy=False)
I_n = sp.eye(len(graph.nodes))
I_min_A = I_n - A
# In scipy.sparse.linalg maxiter defaults to num_nodes * 10, but in testing this sometimes isn't enough which
# crashes instead of settling on whatever eigenvectors are at after maxiters
u, s, vt = lg.svds(I_min_A,
k=self._d + 1,
which='SM',
maxiter=(graph.number_of_nodes() * 1000))
t2 = time()
self._X = vt.T
self._X = self._X[:, 1:]
return self._X.real, (t2 - t1)
def learn_embedding(self, graph=None, edge_f=None, is_weighted=False, no_python=False):
# A = nx.to_scipy_sparse_matrix(G)
# if not np.allclose(A.T, A):
# print "laplace eigmap approach only works for symmetric graphs!"
# return
# self._node_num = A.shape[0]
# D = np.diag(np.sum(A, 1))
# L_G = D - A
# zeroRows = np.where(D.sum(1)==0)
# D[zeroRows, zeroRows] = np.inf
# d_min_half = np.linalg.inv(np.sqrt(D))
# L_sym = np.dot(d_min_half, np.dot(L_G, d_min_half))
if not graph and not edge_f:
raise Exception('graph/edge_f needed')
if not graph:
graph = graph_util.loadGraphFromEdgeListTxt(edge_f)
graph = graph.to_undirected()
t1 = time()
L_sym = nx.normalized_laplacian_matrix(graph)
w, v = lg.eigs(L_sym, k=self._d+1, which='SM')
t2 = time()
self._X = v[:, 1:]
# p_d_p_t = np.dot(v, np.dot(np.diag(w), v.T))
# eig_err = np.linalg.norm(p_d_p_t - L_sym)
# print 'Laplacian matrix reconstruction error (low rank): %f' % eig_err
# p_d_p_t = np.dot(self._X, np.dot(w[1:self._d+1, 1:self._d+1], self._X.T))
# eig_err = np.linalg.norm(p_d_p_t - L_sym)
# print 'Laplacian reconstruction error (low rank approx): %f' % eig_err
return self._X, (t2-t1)
def learn_embedding(self,
graph=None,
edge_f=None,
is_weighted=False,
no_python=False):
if not graph and not edge_f:
raise Exception('graph/edge_f needed')
if not graph:
graph = graph_util.loadGraphFromEdgeListTxt(edge_f)
t1 = time()
# A = nx.to_scipy_sparse_matrix(graph)
# I = sp.eye(graph.number_of_nodes())
# M_g = I - self._beta*A
# M_l = self._beta*A
A = nx.to_numpy_matrix(graph)
M_g = np.eye(graph.number_of_nodes()) - self._beta * A
M_l = self._beta * A
S = np.dot(np.linalg.inv(M_g), M_l)
u, s, vt = lg.svds(S, k=self._d // 2)
X1 = np.dot(u, np.diag(np.sqrt(s)))
X2 = np.dot(vt.T, np.diag(np.sqrt(s)))
t2 = time()
self._X = np.concatenate((X1, X2), axis=1)
p_d_p_t = np.dot(u, np.dot(np.diag(s), vt))
eig_err = np.linalg.norm(p_d_p_t - S)
print('SVD error (low rank): %f' % eig_err)
zipbObj = zip(list(graph.nodes), self._X.tolist())
# Create a dictionary from zip object
models = dict(zipbObj)
return models, (t2 - t1)
def learn_embedding(self,
graph=None,
edge_f=None,
is_weighted=False,
no_python=False):
if not graph and not edge_f:
raise Exception('graph/edge_f needed')
if not graph:
graph = graph_util.loadGraphFromEdgeListTxt(edge_f)
graph = graph.to_undirected()
t1 = time()
L_sym = nx.normalized_laplacian_matrix(graph)
try:
w, v = lg.eigs(L_sym, k=self._d + 1, which='SM')
t2 = time()
self._X = v[:, 1:]
p_d_p_t = np.dot(v, np.dot(np.diag(w), v.T))
eig_err = np.linalg.norm(p_d_p_t - L_sym)
print('Laplacian matrix recon. error (low rank): %f' % eig_err)
return self._X, (t2 - t1)
except:
print('SVD did not converge. Assigning random emebdding')
self._X = np.random.randn(L_sym.shape[0], self._d)
t2 = time()
return self._X, (t2 - t1)
def learn_embedding(self,
graph=None,
edge_f=None,
is_weighted=False,
no_python=False):
if not graph and not edge_f:
raise Exception('graph/edge_f needed')
if not graph:
graph = graph_util.loadGraphFromEdgeListTxt(edge_f)
t1 = time()
# A = nx.to_scipy_sparse_matrix(graph)
# I = sp.eye(graph.number_of_nodes())
# M_g = I - self._beta*A
# M_l = self._beta*A
A = nx.to_numpy_matrix(graph, nodelist=range(graph.number_of_nodes()))
if self._sim_fn == "katz":
M_g = np.eye(graph.number_of_nodes()) - self._beta * A
M_l = self._beta * A
elif self._sim_fn == "pagerank":
# np.matrix can't
A = np.array(A)
# in case the sum is 0
row_sums = A.sum(axis=1) + 1e-8
P = A / row_sums[:, np.newaxis]
M_g = np.eye(graph.number_of_nodes()) - self._beta * P
M_l = (1 - self._beta) * np.eye(graph.number_of_nodes())
elif self._sim_fn == "cn":
M_g = np.eye(graph.number_of_nodes())
M_l = np.dot(A, A)
elif self._sim_fn == "aa":
D = A.sum(axis=1) + A.sum(axis=0)
D = np.diag(np.reciprocal(D.astype('float')))
M_g = np.eye(graph.number_of_nodes())
M_l = np.dot(np.dot(A, D), A)
else:
M_g = np.eye(graph.number_of_nodes()) - self._beta * A
M_l = self._beta * A
try:
S = np.dot(np.linalg.inv(M_g), M_l)
u, s, vt = lg.svds(S, k=self._d // 2)
X1 = np.dot(u, np.diag(np.sqrt(s)))
X2 = np.dot(vt.T, np.diag(np.sqrt(s)))
t2 = time()
self._X = np.concatenate((X1, X2), axis=1)
p_d_p_t = np.dot(u, np.dot(np.diag(s), vt))
eig_err = np.linalg.norm(p_d_p_t - S)
print('SVD error (low rank): %f' % eig_err)
return self._X, (t2 - t1)
except:
print(
'Singularity Matrix or SVD did not converge. Assigning random emebdding'
)
X1 = np.random.randn(A.shape[0], self._d // 2)
X2 = np.random.randn(A.shape[0], self._d // 2)
t2 = time()
self._X = np.concatenate((X1, X2), axis=1)
return self._X, (t2 - t1)
def learn_embedding(self,
graph=None,
edge_f=None,
is_weighted=False,
no_python=False):
if not graph and not edge_f:
raise Exception('graph/edge_f needed')
if not graph:
graph = graph_util.loadGraphFromEdgeListTxt(edge_f)
t1 = time()
nNodes = graph.number_of_nodes()
nEdges = graph.number_of_edges()
print('num nodes: ', nNodes)
print('num edges: ', nEdges)
S = nx.to_numpy_matrix(graph, nodelist=sorted(graph.nodes()))
A = Normalizer(norm='l1').fit_transform(S)
if self._d == None:
self._d = 2 * self._K
else:
assert self._d == 2 * self._K
# Tensorization
md_array = np.zeros((nNodes, nNodes, self._K))
int_res = A
for i in range(self._K):
md_array[:, :, i] = int_res
int_res = int_res.dot(A)
emb = np.zeros((nNodes, self._d))
for i in range(self._K):
print('Slab id: ', i)
slab = np.reshape(md_array[:, :, i], (nNodes, nNodes, 1))
XX = tl.tensor(slab)
print('Tensor shape: ', XX.shape)
factors = parafac(XX,
rank=self._R,
n_iter_max=self._n_iter,
init='random') # random_state=123,
source_emb = factors[0]
target_emb = factors[1]
proximity_emb = factors[2]
print('Source emb shape: ', source_emb.shape)
print('Target emb shape: ', target_emb.shape)
print('Proximity emb shape: ', proximity_emb.shape)
source_proximity_emb = np.dot(source_emb, proximity_emb.T)
target_proximity_emb = np.dot(target_emb, proximity_emb.T)
emb[:, [i, i + self._K]] = np.concatenate(
(source_proximity_emb, target_proximity_emb), axis=1)
self._X = emb
print("Embedding shape: ", self._X.shape)
t2 = time()
return self._X, (t2 - t1)
def tsv2edgelist(file, isDirected=True):
"""
load an edgelist from a tsv file for use in the GEM package
"""
G = (graph_util.loadGraphFromEdgeListTxt('../data/Nedgelist.tsv',
directed=isDirected))
if isDirected:
return G.to_directed()
else:
return G
def learn_embedding(self,
graph=None,
edge_f=None,
is_weighted=False,
no_python=False):
# A = nx.to_scipy_sparse_matrix(G)
# if not np.allclose(A.T, A):
# print "laplace eigmap approach only works for symmetric graphs!"
# return
# self._node_num = A.shape[0]
# D = np.diag(np.sum(A, 1))
# L_G = D - A
# zeroRows = np.where(D.sum(1)==0)
# D[zeroRows, zeroRows] = np.inf
# d_min_half = np.linalg.inv(np.sqrt(D))
# L_sym = np.dot(d_min_half, np.dot(L_G, d_min_half))
if not graph and not edge_f:
raise Exception('graph/edge_f needed')
if not graph:
graph = graph_util.loadGraphFromEdgeListTxt(edge_f)
graph = graph.to_undirected()
t1 = time()
L_sym = nx.normalized_laplacian_matrix(graph)
w, v = lg.eigs(L_sym, k=self._d + 1, which='SM')
t2 = time()
self._X = v[:, 1:]
#p_d_p_t = np.dot(v, np.dot(np.diag(w), v.T))
#eig_err = np.linalg.norm(p_d_p_t - L_sym)
#print 'Laplacian matrix reconstruction error (low rank): %f' % eig_err
#p_d_p_t = np.dot(self._X, np.dot(w[1:self._d+1, 1:self._d+1], self._X.T))
#eig_err = np.linalg.norm(p_d_p_t - L_sym)
#print 'Laplacian reconstruction error (low rank approx): %f' % eig_err
# BLOCCO DI ISTRUZIONI DA ESEGUIRE SE GLI ID DEL DATASET NON SONO COMPATTI
listNodes = graph.nodes()
listNodes = list(set(listNodes)) # Elimina i doppioni dalla lista
listNodes.sort(
) # Ordina la lista che contiene tutti gli ID contenuti nel Grafo originale
nA = np.asarray(listNodes, dtype=int)
dE = self._d
nR = (nA.max()) + 1
XX = np.zeros((nR, dE))
for i in range(0, nA.__len__()):
XX[nA[i]] = cp.copy(self._X[i])
self._X = np.zeros((nR, dE))
self._X = cp.copy(XX)
return self._X, (t2 - t1)
def executeLinkPrediction(dataset, isDirected, method):
G_train = graph_util.loadGraphFromEdgeListTxt(
'gem/datasets/' + dataset + '/u1LIKECompact.base',
directed=isDirected) # Carica il Grafo di Train
G_test = graph_util.loadGraphFromEdgeListTxt(
'gem/datasets/' + dataset + '/u1LIKECompact.test',
directed=isDirected) # Carica il Grafo di Test
G_total = graph_util.loadGraphFromEdgeListTxt(
'gem/datasets/' + dataset + '/u1LIKECompact.edgelist',
directed=isDirected) # Carica il Grafo totale (Train + Test)
MAP = lp.evaluateStaticLinkPrediction(G_total,
G_train,
G_test,
method,
is_undirected=(not isDirected))
print method.get_method_summary()
print "MAP:", MAP
def learn_embedding(self,
graph=None,
edge_f=None,
is_weighted=False,
no_python=False):
if not graph and not edge_f:
raise Exception('graph/edge_f needed')
if not graph:
graph = graph_util.loadGraphFromEdgeListTxt(edge_f)
t1 = time()
# A = nx.to_scipy_sparse_matrix(graph)
# I = sp.eye(graph.number_of_nodes())
# M_g = I - self._beta*A
# M_l = self._beta*A
A = nx.to_numpy_matrix(
graph) # Crea la matrice di adiacenza del Grafo 'graph'
#print "Graph:\n", graph
#print "Matrice di adiacenza del Grafo:\n", A
M_g = np.eye(graph.number_of_nodes()) - self._beta * A
M_l = self._beta * A
S = np.dot(np.linalg.inv(M_g), M_l)
u, s, vt = lg.svds(S, k=self._d // 2)
X1 = np.dot(u, np.diag(np.sqrt(s)))
X2 = np.dot(vt.T, np.diag(np.sqrt(s)))
t2 = time()
self._X = np.concatenate((X1, X2), axis=1)
p_d_p_t = np.dot(u, np.dot(np.diag(s), vt))
eig_err = np.linalg.norm(p_d_p_t - S)
print('SVD error (low rank): %f' % eig_err)
# p_d_p_t = np.dot(self._X, np.dot(w[1:self._d+1, 1:self._d+1], self._X.T))
# eig_err = np.linalg.norm(p_d_p_t - L_sym)
# print 'Laplacian reconstruction error (low rank approx): %f' % eig_err
# BLOCCO DI ISTRUZIONI DA ESEGUIRE SE GLI ID DEL DATASET NON SONO COMPATTI
listNodes = graph.nodes()
listNodes = list(set(listNodes)) # Elimina i doppioni dalla lista
listNodes.sort(
) # Ordina la lista che contiene tutti gli ID contenuti nel Grafo originale
nA = np.asarray(listNodes, dtype=int)
dE = self._d
nR = (nA.max()) + 1
XX = np.zeros((nR, dE))
for i in range(0, nA.__len__()):
XX[nA[i]] = cp.copy(self._X[i])
self._X = np.zeros((nR, dE))
self._X = cp.copy(XX)
return self._X, (t2 - t1)
def run_sdne(edges_file, modified_filename, vertices_filename):
# Instatiate the embedding method with hyperparameters
em = sdne(d=100, beta=5, alpha=1e-6, nu1=1e-3, nu2=1e-3, K=3, n_units=[500, 300], rho=0.3, n_iter=1, xeta=1e-4, n_batch=500, modelfile=['./intermediate/enc_model.json', './intermediate/dec_model.json'], weightfile=['./intermediate/enc_weights.hdf5', './intermediate/dec_weights.hdf5'])
# Load graph
graph = graph_util.loadGraphFromEdgeListTxt(edges_file)
# Learn embedding - accepts a networkx graph or file with edge list
Y, t = em.learn_embedding(graph, edge_f=None, is_weighted=True, no_python=True)
create_converted_file(em.get_embedding(None), modified_filename, vertices_filename)
def main(args):
# Load edgelist
G = graph_util.loadGraphFromEdgeListTxt(args.input, directed=args.directed)
G = G.to_directed()
# Preprocess the graph
# G, _ = prep_graph(G)
if args.method == 'gf':
# GF takes embedding dimension (d), maximum iterations (max_iter), learning rate (eta),
# regularization coefficient (regu) as inputs
model = GraphFactorization(d=args.dimension,
max_iter=args.max_iter,
eta=args.eta,
regu=args.regu)
elif args.method == 'hope':
# HOPE takes embedding dimension (d) and decay factor (beta) as inputs
model = HOPE(d=args.dimension, beta=args.beta)
elif args.method == 'lap':
# LE takes embedding dimension (d) as input
model = LaplacianEigenmaps(d=args.dimension)
elif args.method == 'lle':
# LLE takes embedding dimension (d) as input
model = LocallyLinearEmbedding(d=args.dimension)
elif args.method == 'sdne':
encoder_layer_list = ast.literal_eval(args.encoder_list)
# SDNE takes embedding dimension (d), seen edge reconstruction weight (beta), first order proximity weight
# (alpha), lasso regularization coefficient (nu1), ridge regreesion coefficient (nu2), number of hidden layers
# (K), size of each layer (n_units), number of iterations (n_ite), learning rate (xeta), size of batch (n_batch)
# location of modelfile and weightfile save (modelfile and weightfile) as inputs
model = SDNE(d=args.dimension,
beta=args.beta,
alpha=args.alpha,
nu1=args.nu1,
nu2=args.nu2,
K=len(encoder_layer_list),
n_units=encoder_layer_list,
n_iter=args.max_iter,
xeta=args.learning_rate,
n_batch=args.bs)
# , modelfile=['enc_model.json', 'dec_model.json'], weightfile=['enc_weights.hdf5', 'dec_weights.hdf5'])
else:
raise ValueError('The requested method does not exist!')
# Learn the node embeddings
Y, t = model.learn_embedding(graph=G,
edge_f=None,
is_weighted=args.weighted,
no_python=True)
Z = np.real_if_close(Y, tol=1000)
# Save the node embeddings to a file
np.savetxt(args.output, Z, delimiter=',', fmt='%f')
def prep_embedding(self, graph=None, edge_f=None,
is_weighted=False, no_python=False):
if not graph and not edge_f:
raise Exception('graph/edge_f needed')
if not graph:
graph = graph_util.loadGraphFromEdgeListTxt(edge_f)
S = nx.to_scipy_sparse_matrix(graph)
t1 = time()
S = (S + S.T) / 2
print (S.shape)
self._node_num = graph.number_of_nodes()
print (self._node_num)
def learn_embedding(self,
graph=None,
edge_f=None,
is_weighted=False,
no_python=False):
if not graph and not edge_f:
raise Exception('graph/edge_f needed')
if not graph:
graph = graph_util.loadGraphFromEdgeListTxt(edge_f)
graph = graph.to_undirected()
t1 = time()
self._X = np.random.randn(graph.number_of_nodes(), 1)
t2 = time()
return self._X, (t2 - t1)
def main(data_set_name):
dimensions = 4
input_file = './graph/' + data_set_name + '.tsv'
output_file = './emb/' + data_set_name + '.emb'
# Instatiate the embedding method with hyperparameters
graph_factorization = LaplacianEigenmaps(dimensions)
# Load graph
graph = graph_util.loadGraphFromEdgeListTxt(input_file)
# Learn embedding - accepts a networkx graph or file with edge list
embeddings_array, t = graph_factorization.learn_embedding(graph, edge_f=None, is_weighted=True, no_python=True)
embeddings = pandas.DataFrame(embeddings_array)
embeddings.to_csv(output_file, sep=' ', na_rep=0.1)
def learn_embedding(self, graph=None, edge_f=None,
is_weighted=False, no_python=False):
if not graph and not edge_f:
raise Exception('graph/edge_f needed')
if not graph:
graph = graph_util.loadGraphFromEdgeListTxt(edge_f)
graph = graph.to_undirected()
L_sym = nx.normalized_laplacian_matrix(graph)
w, v = lg.eigs(L_sym, k=self._d + 1, which='SM')
self._X = v[:, 1:]
p_d_p_t = np.dot(v, np.dot(np.diag(w), v.T))
eig_err = np.linalg.norm(p_d_p_t - L_sym)
return self._X, eig_err
def learn_embedding(self, graph=None, edge_f=None, is_weighted=False, no_python=False):
if not graph and not edge_f:
raise Exception('graph/edge_f needed')
if not graph:
graph = graph_util.loadGraphFromEdgeListTxt(edge_f)
graph = graph.to_undirected()
t1 = time()
A = nx.to_scipy_sparse_matrix(graph)
normalize(A, norm='l1', axis=1, copy=False)
I = sp.eye(graph.number_of_nodes())
I_min_A = I - A
u, s, vt = lg.svds(I_min_A, k=self._d+1, which='SM')
t2 = time()
self._X = vt.T
self._X = self._X[:, 1:]
return self._X, (t2-t1)
def learn_embedding(self,
graph=None,
edge_f=None,
is_weighted=False,
no_python=False):
args = ["node2vec"]
if not graph and not edge_f:
raise Exception('graph/edge_f needed')
if graph is None:
graph = graph_util.loadGraphFromEdgeListTxt(edge_f, directed=True)
print("node2vec graph")
print(graph.edges()[:3])
# print(graph)
graph_util.saveGraphToEdgeListTxtn2v(graph, 'tempGraph.graph')
args.append("-i:tempGraph.graph")
args.append("-o:tempGraph.emb")
args.append("-d:%d" % self._d)
args.append("-l:%d" % self._walk_len)
args.append("-r:%d" % self._num_walks)
args.append("-k:%d" % self._con_size)
args.append("-e:%d" % self._max_iter)
args.append("-p:%f" % self._ret_p)
args.append("-q:%f" % self._inout_p)
args.append("-v")
args.append("-dr")
args.append("-w")
t1 = time()
try:
call(args)
except Exception as e:
print(str(e))
raise Exception(
'./node2vec not found. Please compile snap, place node2vec in the system path and grant executable permission'
)
self._X = graph_util.loadEmbedding('tempGraph.emb')
t2 = time()
print('len graph edges')
print(len(graph.nodes()))
print('embedding vectors number')
print(len(self._X))
print('GUESS embedding node2vc')
for i in range(len(self._X)):
print(str(graph.nodes()[i]) + " " + str(self._X[i]))
# print (self._X)
return self._X, (t2 - t1)
def learn_embedding(self,
graph=None,
edge_f=None,
is_weighted=False,
no_python=False):
args = ["gem/c_exe/node2vec"]
if not graph and not edge_f:
raise Exception('graph/edge_f needed')
if edge_f:
graph = graph_util.loadGraphFromEdgeListTxt(edge_f)
graphFileName = 'gem/intermediate/%s_n2v.graph' % self._data_set
embFileName = 'gem/intermediate/%s_%d_n2v.emb' % (self._data_set,
self._d)
try:
f = open(graphFileName, 'r')
f.close()
except IOError:
graph_util.saveGraphToEdgeListTxtn2v(graph, graphFileName)
args.append("-i:%s" % graphFileName)
args.append("-o:%s" % embFileName)
args.append("-d:%d" % self._d)
args.append("-l:%d" % self._walk_len)
args.append("-r:%d" % self._num_walks)
args.append("-k:%d" % self._con_size)
args.append("-e:%d" % self._max_iter)
args.append("-p:%f" % self._ret_p)
args.append("-q:%f" % self._inout_p)
args.append("-v")
args.append("-dr")
args.append("-w")
t1 = time()
try:
call(args)
except Exception as e:
print(str(e))
raise Exception(
'./node2vec not found. Please compile snap, place node2vec in the path and grant executable permission'
)
self._X = graph_util.loadEmbedding(embFileName)
t2 = time()
call(["rm", embFileName])
return self._X, (t2 - t1)
def learn_embedding(self, graph=None, edge_f=None,
is_weighted=False, no_python=False):
if not graph and not edge_f:
raise Exception('graph/edge_f needed')
if not graph:
graph = graph_util.loadGraphFromEdgeListTxt(edge_f)
t1 = time()
# A = nx.to_scipy_sparse_matrix(graph)
# I = sp.eye(graph.number_of_nodes())
# M_g = I - self._beta*A
# M_l = self._beta*A
A = nx.to_numpy_matrix(graph)
#A est l'adjacency matrix sous forme de numpy matrix
M_g = np.eye(graph.number_of_nodes()) - self._beta * A
#identite - 0.01 * A
M_l = self._beta * A
#0.01 * A
S = np.dot(np.linalg.inv(M_g), M_l)
#produit de M_g-1 et M_l
u, s, vt = lg.svds(S, k=self._d // 2)
#valeurs propres et vecteurs propres
X1 = np.dot(u, np.diag(np.sqrt(s)))
X2 = np.dot(vt.T, np.diag(np.sqrt(s)))
t2 = time()
self._X = np.concatenate((X1, X2), axis=1)
test = self._X
p_d_p_t = np.dot(u, np.dot(np.diag(s), vt))
eig_err = np.linalg.norm(p_d_p_t - S)
print('SVD error (low rank): %f' % eig_err)
return self._X, (t2 - t1)
def learn_embedding(self,
graph=None,
edge_f=None,
is_weighted=False,
no_python=False):
if not graph and not edge_f:
raise Exception('graph/edge_f needed')
if not graph:
graph = graph_util.loadGraphFromEdgeListTxt(edge_f)
graph = graph.to_undirected()
t1 = time()
L_sym = nx.normalized_laplacian_matrix(graph)
w, v = lg.eigs(L_sym, k=self._d + 1, which='SM')
idx = np.argsort(w) # sort eigenvalues
w = w[idx]
v = v[:, idx]
t2 = time()
self._X = v[:, 1:]
p_d_p_t = np.dot(v, np.dot(np.diag(w), v.T))
eig_err = np.linalg.norm(p_d_p_t - L_sym)
print('Laplacian matrix recon. error (low rank): %f' % eig_err)
return self._X.real, (t2 - t1)
def learn_embedding(self,
graph=None,
edge_f=None,
amatrix=None,
nodelist=None,
weight='weight'):
if graph is None and edge_f is None and amatrix is None:
raise Exception('graph/edge_f/amatrix needed')
if amatrix is None:
if graph is None:
graph = graph_util.loadGraphFromEdgeListTxt(edge_f)
if nodelist is None:
nodelist = sorted(graph.nodes)
A = nx.to_numpy_matrix(graph, nodelist=nodelist, weight=weight)
n = graph.number_of_nodes()
else:
A = np.mat(amatrix)
n = A.shape[0]
if self._proximity == 'katz':
M_g = np.eye(n) - self._beta * A
M_l = self._beta * A
elif self._proximity == 'common-neighbors':
M_g = np.eye(n)
M_l = A * A
S = np.dot(np.linalg.inv(M_g), M_l)
u, s, vt = lg.svds(S, k=self._d // 2)
X1 = np.dot(u, np.diag(np.sqrt(s)))
X2 = np.dot(vt.T, np.diag(np.sqrt(s)))
self._X = np.concatenate((X1, X2), axis=1)
p_d_p_t = np.dot(u, np.dot(np.diag(s), vt))
eig_err = np.linalg.norm(p_d_p_t - S)
return self._X, eig_err
def learn_embedding(self,
graph=None,
edge_f=None,
is_weighted=False,
no_python=False):
if not graph and not edge_f:
raise Exception('graph/edge_f needed')
if not graph:
graph = graph_util.loadGraphFromEdgeListTxt(edge_f)
graph = graph.to_undirected()
t1 = time()
A = nx.to_scipy_sparse_matrix(graph)
normalize(A, norm='l1', axis=1, copy=False)
I = sp.eye(graph.number_of_nodes())
I_min_A = I - A
u, s, vt = lg.svds(I_min_A, k=self._d + 1, which='SM')
t2 = time()
self._X = vt.T
self._X = self._X[:, 1:]
# BLOCCO DI ISTRUZIONI DA ESEGUIRE SE GLI ID DEL DATASET NON SONO COMPATTI
listNodes = graph.nodes()
listNodes = list(set(listNodes)) # Elimina i doppioni dalla lista
listNodes.sort(
) # Ordina la lista che contiene tutti gli ID contenuti nel Grafo originale
nA = np.asarray(listNodes, dtype=int)
dE = self._d
nR = (nA.max()) + 1
XX = np.zeros((nR, dE))
for i in range(0, nA.__len__()):
XX[nA[i]] = cp.copy(self._X[i])
self._X = np.zeros((nR, dE))
self._X = cp.copy(XX)
return self._X, (t2 - t1)
