diff --git a/comparison.py b/comparison.py
index b9f843a..3749187 100644
--- a/comparison.py
+++ b/comparison.py
@@ -26,7 +26,7 @@ def leverage(g, weight):
         neighbours = g.get_all_neighbours(v)
         ki = len(neighbours)
         # sum
-        for nv, props in neighbours:
+        for nv in neighbours:
             other_neighbours = g.get_all_neighbours(nv)
             kj = len(other_neighbours)
             li += (ki - kj) / (ki + kj)
@@ -104,7 +104,15 @@ def spatial_graph(adata):
 
 def apply(g, seed, weight, convex_hull, ax, method, method_name):
     # calculate centrality values
-    vp = method(g, weight=weight)
+    vp = None
+    if method_name == "Betweeness":
+        vp, ep = method(g, weight=weight)
+    elif method_name == "Eigenvector":
+        ep, vp = method(g, weight=weight)
+    elif method_name == "Hits":
+        ep, vp, hub_centrality = method(g, weight=weight)
+    else:
+        vp = method(g, weight=weight)
     vp.a = np.nan_to_num(vp.a) # correct floating point values
 
     # normalization
@@ -128,7 +136,7 @@ def apply(g, seed, weight, convex_hull, ax, method, method_name):
         [lambda x: m_opt * x + c0_opt, lambda x: m_opt * b_opt + c0_opt]
     )
     # plot model containing modeled piece-wise linear function
-    plot.quantification_plot(ax, quantification, d_curve, C_curve, 'Models', aic_opt)
+    plot.quantification_plot(ax, quantification, d_curve, C_curve, method_name, aic_opt)
 
 
 #
@@ -139,30 +147,33 @@ def apply(g, seed, weight, convex_hull, ax, method, method_name):
 #   - apply centrality measure to the next axis
 # - Draw the corresponding resulting models into a grid
 #
-points, seed = random_graph()
+points, seed = random_graph(n=5000)
 g, weight = spatial_graph(points)
 g = GraphView(g)
 # calculate convex hull
 convex_hull = centrality.convex_hull(g)
 
 # plot graph with convex_hull
-fig_graph, ax_graph = plt.subplots(figsize=(15, 5))
+fig_graph, ax_graph = plt.subplots(figsize=(15, 12))
 # draw without any centrality measure `vp`
-plot.graph_plot(fig_graph, ax_graph, g, vp, convex_hull, f"Pointcloud (seed: {seed}\n{method_name}")
+vp = g.new_vertex_property("double")
+plot.graph_plot(fig_graph, ax_graph, g, vp, convex_hull, f"Pointcloud (seed: {seed}")
 fig_graph.savefig("Pointcloud_graph.svg", format='svg')
 
-fig = plt.figure(figsize=(15, 10))
-axs = fig.subplots(2, 4)
+fig = plt.figure(figsize=(15, 12))
+row1, row2 = fig.subplots(2, 4)
 
-for ax in axs:
-    # TODO select corresponding centrality measure method
-    apply(g, seed, weight, convex_hull, ax, closeness, "Closeness")
-    apply(g, seed, weight, convex_hull, ax, pagerank, "PageRank")
-    apply(g, seed, weight, convex_hull, ax, betweeness, "Betweeness") 
-    apply(g, seed, weight, convex_hull, ax, eigenvector, "Eigenvector") 
-    apply(g, seed, weight, convex_hull, ax, katz, "Katz")
-    apply(g, seed, weight, convex_hull, ax, hits, "Hits")
-    apply(g, seed, weight, convex_hull, ax, leverage, "Leverage")
-    apply(g, seed, weight, convex_hull, ax, degree, "Degree")
+ax1, ax2, ax3, ax4 = row1
+# TODO select corresponding centrality measure method
+apply(g, seed, weight, convex_hull, ax1, closeness, "Closeness")
+apply(g, seed, weight, convex_hull, ax2, pagerank, "PageRank")
+apply(g, seed, weight, convex_hull, ax3, betweenness, "Betweeness")
+apply(g, seed, weight, convex_hull, ax4, eigenvector, "Eigenvector")
+
+ax1, ax2, ax3, ax4 = row2
+apply(g, seed, weight, convex_hull, ax1, katz, "Katz")
+apply(g, seed, weight, convex_hull, ax2, hits, "Hits")
+apply(g, seed, weight, convex_hull, ax3, leverage, "Leverage")
+apply(g, seed, weight, convex_hull, ax4, degree, "Degree")
 
 fig.savefig(f"Comparison_Pointcloud.svg", format='svg')