grarep_checked_v0.0

src/libnrl/grarep.py

@@ -1,7 +1,16 @@
+"""
+a matrix factorization based NE method: GraRep
+
+modified by Chengbin Hou 2018
+
+originally from https://github.com/thunlp/OpenNE/blob/master/src/openne/grarep.py
+"""
+
 import math
 import numpy as np
 from numpy import linalg as la
 from sklearn.preprocessing import normalize
+from .utils import row_as_probdist
 
 class GraRep(object):
     
@@ -13,6 +22,7 @@ class GraRep(object):
         self.train()
 
     def getAdjMat(self):
+        '''
         graph = self.g.G
         node_size = self.g.get_num_nodes()
         look_up = self.g.look_up_dict
@@ -22,6 +32,9 @@ class GraRep(object):
             adj[look_up[edge[1]]][look_up[edge[0]]] = 1.0
         # ScaleSimMat
         return np.matrix(adj/np.sum(adj, axis=1))
+        '''
+        adj = self.g.get_adj_mat() #for isolated node row, normalize to [1/n, 1/n, ...]
+        return row_as_probdist(adj, dense_output=True, preserve_zeros=False)
 
     def GetProbTranMat(self, Ak):
         probTranMat = np.log(Ak/np.tile(

src/main.py

@@ -91,7 +91,7 @@ def parse_args():
     parser.add_argument('--Node2Vec-q', default=0.5, type=float,
                         help='trade-off BFS and DFS; rid search [0.25; 0.50; 1; 2; 4]')
     parser.add_argument('--GraRep-kstep', default=4, type=int,
-                        help='use k-step transition probability matrix')
+                        help='use k-step transition probability matrix, error if dim%Kstep!=0')
     parser.add_argument('--LINE-order', default=3, type=int, 
                         help='choices of the order(s), 1st order, 2nd order, 1st+2nd order')
     parser.add_argument('--LINE-no-auto-save', action='store_true',