initial codebase

.gitignore

@@ -0,0 +1 @@
+.pyc

src/main.py

@@ -1,14 +1,19 @@
 '''
+Reference implementation of node2vec. 
 
+Author: Aditya Grover
 
-
+For more details, refer to the paper:
+node2vec: Scalable Feature Learning for Networks
+Aditya Grover and Jure Leskovec 
+Knowledge Discovery and Data Mining (KDD), 2016
 '''
 
-
 import argparse
 import numpy as np
 import networkx as nx
 import node2vec
+from gensim.models import Word2Vec
 
 def parse_args():
 	'''

src/node2vec.py

@@ -7,12 +7,15 @@
 # for details
 
 def alias_setup(probs):
-    K       = len(probs)
-    q       = np.zeros(K)
-    J       = np.zeros(K, dtype=np.int)
+	'''
+	Compute utility lists for non-uniform sampling from discrete distributions.
+	'''
+    K = len(probs)
+    q = np.zeros(K)
+    J = np.zeros(K, dtype=np.int)
 
     smaller = []
-    larger  = []
+    larger = []
     for kk, prob in enumerate(probs):
         q[kk] = K*prob
         if q[kk] < 1.0:
@@ -34,7 +37,10 @@ def alias_setup(probs):
     return J, q
 
 def alias_draw(J, q):
-    K  = len(J)
+	'''
+	Draw sample from a non-uniform discrete distribution using alias sampling.
+	'''
+    K = len(J)
 
     kk = int(np.floor(np.random.rand()*K))
     if np.random.rand() < q[kk]: