diff --git a/figures/d-cliques-mnist-scaling-fractal-cliques-cst-updates.png b/figures/d-cliques-mnist-scaling-fractal-cliques-cst-updates.png
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diff --git a/figures/d-cliques-mnist-scaling-fully-connected-cst-bsz.png b/figures/d-cliques-mnist-scaling-fully-connected-cst-bsz.png
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diff --git a/figures/d-cliques-mnist-scaling-fully-connected-cst-updates.png b/figures/d-cliques-mnist-scaling-fully-connected-cst-updates.png
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diff --git a/main.tex b/main.tex
index c3d0d2ad911b9c655774cbc388bf43f1765d0d5c..2342654774c8498f490c8ee369775026858034e5 100644
--- a/main.tex
+++ b/main.tex
@@ -312,13 +312,21 @@ We solve this problem by decoupling the gradient averaging from the weight avera
 %     \end{subfigure}
      
               % To regenerate the figure, from directory results/scaling
-% python ../../../learn-topology/tools/plot_convergence.py 10/mnist/fully-connected-cliques/all/2021-03-12-09:13:27-CET ../mnist/fully-connected-cliques/all/2021-03-10-10:19:44-CET 1000/mnist/fully-connected-cliques/all/2021-03-12-09:13:28-CET --labels '10 nodes bsz=1280' '100 nodes bsz=128' '1000 nodes bsz=13' --legend 'lower right' --yaxis test-accuracy --save-figure ../../figures/d-cliques-mnist-scaling-fully-connected-cst-updates.png --ymin 80 --add-min-max
+% python ../../../learn-topology/tools/plot_convergence.py 10/mnist/fully-connected-cliques/all/2021-03-12-09:13:27-CET ../mnist/fully-connected-cliques/all/2021-03-10-10:19:44-CET 1000/mnist/fully-connected-cliques/all/2021-03-14-17:56:26-CET --labels '10 nodes bsz=1280' '100 nodes bsz=128' '1000 nodes bsz=13' --legend 'lower right' --yaxis test-accuracy --save-figure ../../figures/d-cliques-mnist-scaling-fully-connected-cst-updates.png --ymin 80 --add-min-max
 
       \begin{subfigure}[b]{0.7\textwidth}
          \centering
          \includegraphics[width=\textwidth]{figures/d-cliques-mnist-scaling-fully-connected-cst-updates}
          \caption{Fully-Connected}
      \end{subfigure}
+     
+          % To regenerate the figure, from directory results/scaling
+% python ../../../learn-topology/tools/plot_convergence.py 10/mnist/clique-ring/all/2021-03-13-18:22:01-CET ../mnist/fully-connected-cliques/all/2021-03-10-10:19:44-CET 1000/mnist/fractal-cliques/all/2021-03-14-17:41:59-CET --labels '10 nodes bsz=1280' '100 nodes bsz=128' '1000 nodes bsz=13' --legend 'lower right' --yaxis test-accuracy --save-figure ../../figures/d-cliques-mnist-scaling-fractal-cliques-cst-updates.png --ymin 80 --add-min-max
+         \begin{subfigure}[b]{0.7\textwidth}
+         \centering
+         \includegraphics[width=\textwidth]{figures/d-cliques-mnist-scaling-fractal-cliques-cst-updates}
+         \caption{Fractal}
+     \end{subfigure}  
 
      
      % To regenerate the figure, from directory results/scaling
@@ -332,9 +340,7 @@ We solve this problem by decoupling the gradient averaging from the weight avera
      \caption{\label{fig:d-cliques-mnist-scaling-fully-connected} MNIST: D-Clique Scaling Behaviour (Constant Updates per Epoch)}
      \end{figure}
      
-Show scaling effect for 10, 100, 1000 nodes (with decreasing sample sizes) for Clique Ring, Hierarchical, Fully-Connected.
-
-(Smallworld?)
+Show scaling effect for 10, 100, 1000 nodes (with decreasing sample sizes) for Clique Ring, Fractal, Fully-Connected.
 
 Robustness to not having fully-connected cliques (static and dynamic subsets).