def get_embeddings(self, inst, th=1):
G = nx.parse_edgelist(self._compose_edge_list(inst.dist_mat, th), create_using=nx.DiGraph(), nodetype=None,
data=[('weight', float)])
if self._embedding == 'deepwalk':
model = DeepWalk(G, walk_length=10, num_walks=80, workers=1)
model.train(window_size=5, iter=3)
elif self._embedding == 'node2vec':
model = Node2Vec(G, walk_length=10, num_walks=80, p=0.25, q=4, workers=1) # init model
model.train(window_size=5, iter=3) # train model
elif self._embedding == 'line':
model = LINE(G, embedding_size=128, order='second') # init model,order can be ['first','second','all']
model.train(batch_size=1024, epochs=50, verbose=2) # train model
elif self._embedding == 'sdne':
model = SDNE(G, hidden_size=[256, 128]) # init model
model.train(batch_size=3000, epochs=40, verbose=2) # train model
elif self._embedding == 'struc2vec':
model = Struc2Vec(G, 10, 80, workers=4, verbose=40, ) # init model
model.train(window_size=5, iter=3) # train model
else:
return self._normalise(inst)
ebds = model.get_embeddings()
coords = []
for i in range(inst.n):
coords.append(ebds[str(i)])
return np.array(coords)
color_idx = {}
for i in range(len(X)):
color_idx.setdefault(Y[i][0], [])
color_idx[Y[i][0]].append(i)
for c, idx in color_idx.items():
plt.scatter(node_pos[idx, 0], node_pos[idx, 1],
label=c) # c=node_colors)
plt.legend()
plt.show()
if __name__ == "__main__":
G = nx.read_edgelist('../data/flight/brazil-airports.edgelist',
create_using=nx.DiGraph(),
nodetype=None,
data=[('weight', int)])
# nx.draw(G, node_size=10, font_size=10, font_color='blue', font_weight='bold')
# plt.show()
model = Struc2Vec(
G,
10,
80,
workers=4,
verbose=40,
)
model.train(embed_size=64)
embeddings = model.get_embeddings()
evaluate_embeddings(embeddings)
plot_embeddings(embeddings)
f'../save/graph_embedding_{name}_{graph_name}_svd{svd_dim}.csv')
if __name__ == "__main__":
G = nx.read_edgelist(graph_path,
create_using=nx.DiGraph(),
nodetype=None,
data=[('weight', int)])
model = DeepWalk(G,
walk_length=dw_walk_length,
num_walks=dw_num_walks,
workers=NUM_WORKER) # init model
model.train(window_size=dw_window_size, iter=dw_iter) # train model
embeddings = model.get_embeddings() # get embedding vectors
save_embedding(embeddings, name='dw')
save_embedding_svd(embeddings, name='dw', svd_dim=svd_dim)
model = model = Struc2Vec(G,
walk_length=s2v_walk_length,
num_walks=s2v_num_walks,
workers=NUM_WORKER,
verbose=40) # init model
model.train(window_size=s2v_window_size, iter=s2v_iter) # train model
embeddings = model.get_embeddings() # get embedding vectors
save_embedding(embeddings, name='s2v')
save_embedding_svd(embeddings, name='s2v', svd_dim=svd_dim)
