re-generated dataset

README.md

@@ -100,16 +100,13 @@ Positive and negative graphs are approximately balanced across splits.
 Node counts depend on the underlying base generator and motif size:
 
 - `ba_shapes` (Barabási–Albert, `num_edges = 2`): 8–20 nodes.
+  ![BA shapes node counts](fig/ba_shapes/node_count_hist.svg)
 - `tree_cycle` (Prüfer trees + 6-cycle motif): 8–21 nodes.
+  ![Tree cycle node counts](fig/tree_cycle/node_count_hist.svg)
 - `tree_grid` (Prüfer trees + 3×3 grid motif): 8–24 nodes.
+  ![Tree grid node counts](fig/tree_grid/node_count_hist.svg)
 
-Observed frequencies across the full 2,000-example splits for each subset are shown in the per-subset histograms:
-
-![BA shapes node counts](fig/ba_shapes/node_count_hist.pdf)
-
-![Tree cycle node counts](fig/tree_cycle/node_count_hist.pdf)
-
-![Tree grid node counts](fig/tree_grid/node_count_hist.pdf)
+Observed frequencies across the full 2,000-example splits for each subset are shown in the per-subset histograms.
 
 ## Data Creation
 

gen_all.sh

@@ -0,0 +1,3 @@
+for subset in ba_shapes tree_cycle tree_grid; do
+	python generator.py --subset $subset
+done

generator.py

@@ -91,7 +91,7 @@ def visualize_graphs(pos_ds, neg_ds, subset: str, num_examples: int = 5):
         visualize_graph(neg_ds[i], filename=f"{OUT_DIR}/neg_graph_{i}.pdf")
     print("Saved example graph visualizations to 'fig/' directory.")
 
-    plot_node_histograms(pos_ds, neg_ds, os.path.join(OUT_DIR, "node_count_hist.pdf"))
+    plot_node_histograms(pos_ds, neg_ds, os.path.join(OUT_DIR, "node_count_hist.svg"))
 
 
 def add_motif_nodes(example: Data):