updated ## Working with Graph Topology part

README.md

@@ -104,14 +104,23 @@ print(ds["train"][0])
 
 ### Working with Graph Topology
 ```python
-import networkx as nx
+import torch
+from torch_geometric.data import Data
+from src.utils import visualize_graph
 
 example = ds["train"][0]
-G = nx.Graph()
-G.add_nodes_from(example["nodes"])
-G.add_edges_from({tuple(edge) for edge in example["edges"]})
-motif_nodes = set(example["motif_nodes"])
+
+# Build a PyG data object from the serialized edge list.
+edge_index = torch.tensor(example["edges"], dtype=torch.long).t().contiguous()
+graph = Data(edge_index=edge_index, num_nodes=example["nnodes"])
+
+# Attach the motif mask expected by `visualize_graph`.
+graph.node_mask = torch.zeros(example["nnodes"])
+graph.node_mask[example["motif_nodes"]] = 1
+
+visualize_graph(graph, "motif_example.png")
 ```
+The script saves a PNG with motif nodes highlighted in orange. Use absolute imports (e.g., `from motif_qa.src.utils import visualize_graph`) if you install the project as a package.
 
 ### Regenerating the Dataset Locally
 ```bash