diff --git a/lang/zh/gklearn/kernels/random_walk.py b/lang/zh/gklearn/kernels/random_walk.py
new file mode 100644
index 0000000..f2d0961
--- /dev/null
+++ b/lang/zh/gklearn/kernels/random_walk.py
@@ -0,0 +1,94 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Aug 19 16:55:17 2020
+
+@author: ljia
+
+@references: 
+
+	[1] S Vichy N Vishwanathan, Nicol N Schraudolph, Risi Kondor, and Karsten M Borgwardt. Graph kernels. Journal of Machine Learning Research, 11(Apr):1201–1242, 2010.
+"""
+
+import sys
+from tqdm import tqdm
+import numpy as np
+import networkx as nx
+from gklearn.utils import SpecialLabel
+from gklearn.utils.parallel import parallel_gm, parallel_me
+from gklearn.utils.utils import direct_product_graph
+from gklearn.kernels import GraphKernel
+
+
+class RandomWalk(GraphKernel):
+	
+	
+	def __init__(self, **kwargs):
+		GraphKernel.__init__(self)		
+		self._compute_method = kwargs.get('compute_method', None)
+		self._weight = kwargs.get('weight', 1)
+		self._p = kwargs.get('p', None)
+		self._q = kwargs.get('q', None)
+		self._edge_weight = kwargs.get('edge_weight', None)
+		self._ds_infos = kwargs.get('ds_infos', {})
+		
+		self._compute_method = self.__compute_method.lower()
+		
+		
+	def _compute_gm_series(self):
+		pass
+
+
+	def _compute_gm_imap_unordered(self):
+		pass
+	
+		
+	def _compute_kernel_list_series(self, g1, g_list):
+		pass
+
+	
+	def _compute_kernel_list_imap_unordered(self, g1, g_list):
+		pass
+	
+	
+	def _compute_single_kernel_series(self, g1, g2):
+		pass
+	
+	
+	def _check_graphs(self, Gn):
+		# remove graphs with no edges, as no walk can be found in their structures, 
+		# so the weight matrix between such a graph and itself might be zero.
+		for g in Gn:
+			if nx.number_of_edges(g) == 0:
+				raise Exception('Graphs must contain edges to construct weight matrices.')
+				
+	
+	def _check_edge_weight(self, G0, verbose):
+		eweight = None
+		if self._edge_weight == None:
+			if verbose >= 2:
+				print('\n None edge weight is specified. Set all weight to 1.\n')
+		else:
+			try:
+				some_weight = list(nx.get_edge_attributes(G0, self._edge_weight).values())[0]
+				if isinstance(some_weight, float) or isinstance(some_weight, int):
+					eweight = self._edge_weight
+				else:
+					if verbose >= 2:
+						print('\n Edge weight with name %s is not float or integer. Set all weight to 1.\n' % self._edge_weight)
+			except:
+				if verbose >= 2:
+					print('\n Edge weight with name "%s" is not found in the edge attributes. Set all weight to 1.\n' % self._edge_weight)
+		
+		self._edge_weight = eweight
+				
+		
+	def _add_dummy_labels(self, Gn):
+		if len(self.__node_labels) == 0 or (len(self.__node_labels) == 1 and self.__node_labels[0] == SpecialLabel.DUMMY):
+			for i in range(len(Gn)):
+				nx.set_node_attributes(Gn[i], '0', SpecialLabel.DUMMY)
+			self.__node_labels = [SpecialLabel.DUMMY]
+		if len(self.__edge_labels) == 0 or (len(self.__edge_labels) == 1 and self.__edge_labels[0] == SpecialLabel.DUMMY):
+			for i in range(len(Gn)):
+				nx.set_edge_attributes(Gn[i], '0', SpecialLabel.DUMMY)
+			self.__edge_labels = [SpecialLabel.DUMMY]
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