import math
import numpy as np
from numpy import linalg as la
from sklearn.preprocessing import normalize
class GraRep(object):
def __init__(self, graph, Kstep, dim):
self.g = graph
self.Kstep = Kstep
assert dim % Kstep == 0
self.dim = int(dim/Kstep)
self.train()
def getAdjMat(self):
graph = self.g.G
node_size = self.g.node_size
look_up = self.g.look_up_dict
adj = np.zeros((node_size, node_size))
for edge in self.g.G.edges():
adj[look_up[edge[0]]][look_up[edge[1]]] = 1.0
adj[look_up[edge[1]]][look_up[edge[0]]] = 1.0
# ScaleSimMat
return np.matrix(adj/np.sum(adj, axis=1))
def GetProbTranMat(self, Ak):
probTranMat = np.log(Ak/np.tile(
np.sum(Ak, axis=0), (self.node_size, 1))) \
- np.log(1.0/self.node_size)
probTranMat[probTranMat < 0] = 0
probTranMat[probTranMat == np.nan] = 0
return probTranMat
def GetRepUseSVD(self, probTranMat, alpha):
U, S, VT = la.svd(probTranMat)
Ud = U[:, 0:self.dim]
Sd = S[0:self.dim]
return np.array(Ud)*np.power(Sd, alpha).reshape((self.dim))
def save_embeddings(self, filename):
fout = open(filename, 'w')
node_num = len(self.vectors.keys())
fout.write("{} {}\n".format(node_num, self.Kstep*self.dim))
for node, vec in self.vectors.items():
fout.write("{} {}\n".format(node, ' '.join([str(x) for x in vec])))
fout.close()
def train(self):
self.adj = self.getAdjMat()
self.node_size = self.adj.shape[0]
self.Ak = np.matrix(np.identity(self.node_size))
self.RepMat = np.zeros((self.node_size, int(self.dim*self.Kstep)))
for i in range(self.Kstep):
print('Kstep =', i)
self.Ak = np.dot(self.Ak, self.adj)
probTranMat = self.GetProbTranMat(self.Ak)
Rk = self.GetRepUseSVD(probTranMat, 0.5)
Rk = normalize(Rk, axis=1, norm='l2')
self.RepMat[:, self.dim*i:self.dim*(i+1)] = Rk[:, :]
# get embeddings
self.vectors = {}
look_back = self.g.look_back_list
for i, embedding in enumerate(self.RepMat):
self.vectors[look_back[i]] = embedding
