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loading graph into heterodata object

graph_data.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f30a508301d431958d9293321ecef6f86715738a949dd3fdbbd026032e4ceabf
+size 1161429674

learn_graph.py

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+import json
+import torch
+from torch_geometric.data import HeteroData
+
+# # Load the JSON data
+with open("papers.json", "r") as f:
+    data = json.load(f)
+
+node_features = []
+edges = []
+
+for i, node in enumerate(data):
+    # if i > 1000: break
+    node_features.append((node['index'], node["embedding"]))
+    for reference in node['references']:
+        edges.append((node['index'], reference))
+
+# Create tensors for node features and edges
+node_features = sorted(node_features, key=lambda x: x[0])
+node_features_tensor = torch.tensor([x[1] for x in node_features], dtype=torch.float)
+edge_index_tensor = torch.tensor(edges, dtype=torch.long).t().contiguous()
+
+# Create the HeteroData object
+graph = HeteroData()
+graph['paper'].x = node_features_tensor
+graph['paper', 'cites', 'paper'].edge_index = edge_index_tensor
+
+
+torch.save(graph, "graph_data.pt")
+
+# cited = {}
+# for edge in edges:
+#     if edge[1] not in cited:
+#         cited[edge[1]] = 0
+#     cited[edge[1]] += 1
+    
+# print(cited)
+
+# print(graph)
+
+# # Load the HeteroData object from a file
+# loaded_graph = torch.load("graph_data.pt")
+# print(loaded_graph)