diff --git a/README.md b/README.md
index 386d9a4..085084d 100644
--- a/README.md
+++ b/README.md
@@ -12,12 +12,12 @@ A Python package for graph kernels, graph edit distances and graph pre-image pro
 * python>=3.5
 * numpy>=1.16.2
 * scipy>=1.1.0
-* matplotlib>=3.0.0
+* matplotlib>=3.1.0
 * networkx>=2.2
 * scikit-learn>=0.20.0
 * tabulate>=0.8.2
 * tqdm>=4.26.0
-* control==0.8.0 (for generalized random walk kernels only)
+* control>=0.8.2 (for generalized random walk kernels only)
 * slycot==0.3.3 (for generalized random walk kernels only, which requires a fortran compiler, gfortran for example)
 
 ## How to use?
diff --git a/gklearn/experiments/papers/PRL_2020/synthesized_graphs_num_el.py b/gklearn/experiments/papers/PRL_2020/synthesized_graphs_num_el.py
new file mode 100644
index 0000000..2341ba9
--- /dev/null
+++ b/gklearn/experiments/papers/PRL_2020/synthesized_graphs_num_el.py
@@ -0,0 +1,54 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Sep 21 10:34:26 2020
+
+@author: ljia
+"""
+from utils import Graph_Kernel_List_ESym, compute_graph_kernel
+
+
+def generate_graphs(num_el_alp):
+	from gklearn.utils.graph_synthesizer import GraphSynthesizer
+	gsyzer = GraphSynthesizer()
+	graphs = gsyzer.unified_graphs(num_graphs=100, num_nodes=20, num_edges=40, num_node_labels=0, num_edge_labels=num_el_alp, seed=None, directed=False)
+	return graphs
+
+
+def xp_synthesied_graphs_num_edge_label_alphabet():
+		
+	# Run and save.
+	import pickle
+	import os
+	save_dir = 'outputs/synthesized_graphs_num_edge_label_alphabet/'
+	if not os.path.exists(save_dir):
+		os.makedirs(save_dir)
+
+	run_times = {}
+	
+	for kernel_name in Graph_Kernel_List_ESym:
+		print()
+		print('Kernel:', kernel_name)
+		
+		run_times[kernel_name] = []
+		for num_el_alp in [0, 4, 8, 12, 16, 20, 24, 28, 32, 36, 40]:
+			print()
+			print('Number of edge label alphabet:', num_el_alp)
+			
+			# Generate graphs.
+			graphs = generate_graphs(num_el_alp)
+
+			# Compute Gram matrix.
+			gram_matrix, run_time = compute_graph_kernel(graphs, kernel_name)
+			run_times[kernel_name].append(run_time)
+			
+			pickle.dump(run_times, open(save_dir + 'run_time.' + kernel_name + '.' + str(num_el_alp) + '.pkl', 'wb'))
+		
+	# Save all.	
+	pickle.dump(run_times, open(save_dir + 'run_times.pkl', 'wb'))	
+	
+	return
+
+
+if __name__ == '__main__':
+	xp_synthesied_graphs_num_edge_label_alphabet()
diff --git a/gklearn/experiments/papers/PRL_2020/synthesized_graphs_num_nl.py b/gklearn/experiments/papers/PRL_2020/synthesized_graphs_num_nl.py
index fbe0fa3..005ab35 100644
--- a/gklearn/experiments/papers/PRL_2020/synthesized_graphs_num_nl.py
+++ b/gklearn/experiments/papers/PRL_2020/synthesized_graphs_num_nl.py
@@ -31,7 +31,7 @@ def xp_synthesied_graphs_num_node_label_alphabet():
 		print('Kernel:', kernel_name)
 		
 		run_times[kernel_name] = []
-		for num_nl_alp in [0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20]: # [0, 4, 8, 12, 16, 20, 24, 28, 32, 36, 40]:
+		for num_nl_alp in [0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20]:
 			print()
 			print('Number of node label alphabet:', num_nl_alp)
 			
diff --git a/gklearn/utils/graph_synthesizer.py b/gklearn/utils/graph_synthesizer.py
index 581ebc0..2c5f650 100644
--- a/gklearn/utils/graph_synthesizer.py
+++ b/gklearn/utils/graph_synthesizer.py
@@ -29,9 +29,9 @@ class GraphSynthesizer(object):
 
 		if num_edge_labels > 0:
 			edge_labels = np.random.randint(0, high=num_edge_labels, size=num_edges)				
-			for i in random.sample(range(0, max_num_edges), num_edges):
+			for idx, i in enumerate(random.sample(range(0, max_num_edges), num_edges)):
 				node1, node2 = all_edges[i]
-				g.add_edge(str(node1), str(node2), bond_type=edge_labels[i])  # @todo: update "bond_type".
+				g.add_edge(str(node1), str(node2), bond_type=edge_labels[idx])  # @todo: update "bond_type".
 		else:
 			for i in random.sample(range(0, max_num_edges), num_edges):
 				node1, node2 = all_edges[i]