switch figure 6 and 7 order

to match rearrangement done at
#69 (comment)

content/02.body.md

@@ -237,20 +237,6 @@ The XSwap-derived edge prior reconstructed many of the networks with a high leve
 Of the 20 individual networks we extracted from Hetionet, 17 had an edge prior self-reconstruction AUROC >= 0.95, with the highest reconstruction AUROC at 0.9971 (network was the Compound–downregulates–Gene edge type).
 Meanwhile, the lowest self-reconstruction performance (AUROC = 0.7697) occurred in the network having the fewest node pairs (network was the Disease–localizes–Anatomy edge type).
 
-![
-  **Degree can predict edges within a given network but does not generalize to networks with different degree distributions**
-  The edge prior is able to reconstruct the networks on which it was computed (task 1, "unsampled", 20 different networks) with high performance.
-  When computed on a sampled network, the edge prior can reconstruct the unsampled network with slightly lower performance (task 2, "sampled", 20 different networks).
-  However, when computed on a completely different network (having a different degree distribution) of the same type of data, the edge prior's performance is greatly reduced (task 3, "separate", 3 different networks).
-  The performance reduction from computing predictors on sampled networks is real but far smaller compared to a new degree distribution.
-  This indicates that while degree can be effective for network reconstruction, it is far less effective in predicting edges from a different degree distribution.
-](https://github.com/greenelab/xswap-analysis/raw/4f06bdaf1f034af9136e25c03f9891a145b9bf91/img/auroc_dists.png){#fig:discrimination width="60%"}
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-<!--
-fig:discrimination is created by
-https://github.com/greenelab/xswap-analysis/blob/4f06bdaf1f034af9136e25c03f9891a145b9bf91/nb/5.fig3.auroc/plot_auroc.ipynb
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 The three predictors that we compared were highly correlated (Spearman rank correlation over 0.984 for all 20 networks).
 The three predictors also had very similar AUROC reconstruction performance values for the first, second, and third prediction tasks (max difference < 0.027) because AUROC is rank based.
 The edge prior was slightly better than the approximations in 12 of 20 networks.
@@ -283,6 +269,20 @@ As was observed in the first task, node pair predictors computed in the second t
 While performance was slightly lower in the second task than the first, many networks were still well reconstructed.
 The edge prior was the best calibrated predictor for both tasks.
 
+![
+  **Degree can predict edges within a given network but does not generalize to networks with different degree distributions.**
+  The edge prior is able to reconstruct the networks on which it was computed (task 1, "unsampled", 20 different networks) with high performance.
+  When computed on a sampled network, the edge prior can reconstruct the unsampled network with slightly lower performance (task 2, "sampled", 20 different networks).
+  However, when computed on a completely different network (having a different degree distribution) of the same type of data, the edge prior's performance is greatly reduced (task 3, "separate", 3 different networks).
+  The performance reduction from computing predictors on sampled networks is real but far smaller compared to a new degree distribution.
+  This indicates that while degree can be effective for network reconstruction, it is far less effective in predicting edges from a different degree distribution.
+](https://github.com/greenelab/xswap-analysis/raw/4f06bdaf1f034af9136e25c03f9891a145b9bf91/img/auroc_dists.png){#fig:discrimination width="60%"}
+
+<!--
+fig:discrimination is created by
+https://github.com/greenelab/xswap-analysis/blob/4f06bdaf1f034af9136e25c03f9891a145b9bf91/nb/5.fig3.auroc/plot_auroc.ipynb
+-->
+
 In the third prediction task, we computed the three edge predictors for paired networks representing data from PPI, TF-TG, and bioRxiv bioinformatics preprint coauthorship.
 The goal of the task was to compare predictive performance across different degree distributions for the same type of data.
 We find that the task of predicting systematically derived edges using a network with degree bias is significantly more challenging than network reconstruction, and we find consistently lower performance compared to the other tasks (Figure {@fig:discrimination}).