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hope.py
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hope.py
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# pylint: disable=e1101
import networkx as nx
import numpy as np
import scipy.io as sio
import scipy.sparse as sp
import scipy.sparse.linalg as lg
from . import graph as g
import tensorflow as tf
from sklearn.preprocessing import normalize
__author__ = "Alan WANG"
__email__ = "alan1995wang@outlook.com"
class HOPE(object):
def __init__(self, graph, d):
'''
d: representation vector dimension
'''
self._d = d
self._graph = graph.G
self.g = graph
self._node_num = graph.node_size
self.learn_embedding()
def learn_embedding(self):
graph = self.g.G
A = nx.to_numpy_matrix(graph)
# self._beta = 0.0728
# M_g = np.eye(graph.number_of_nodes()) - self._beta * A
# M_l = self._beta * A
M_g = np.eye(graph.number_of_nodes())
M_l = np.dot(A, A)
S = np.dot(np.linalg.inv(M_g), M_l)
# s: \sigma_k
u, s, vt = lg.svds(S, k=self._d // 2)
sigma = np.diagflat(np.sqrt(s))
X1 = np.dot(u, sigma)
X2 = np.dot(vt.T, sigma)
# self._X = X2
self._X = np.concatenate((X1, X2), axis=1)
@property
def vectors(self):
vectors = {}
look_back = self.g.look_back_list
for i, embedding in enumerate(self._X):
vectors[look_back[i]] = embedding
return vectors
def save_embeddings(self, filename):
fout = open(filename, 'w')
node_num = len(self.vectors.keys())
fout.write("{} {}\n".format(node_num, self._d))
for node, vec in self.vectors.items():
fout.write("{} {}\n".format(node,
' '.join([str(x) for x in vec])))
fout.close()