chore: import upstream snapshot with attribution

2026-07-13 13:35:51 +08:00
commit c36a561cd8
2172 changed files with 455595 additions and 0 deletions
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+import numpy as np
+import pandas as pd
+from dgl import DGLGraph
+
+# from dgl.data.qm9 import QM9
+from dgl.data import CitationGraphDataset, PPIDataset, RedditDataset, TUDataset
+from dgl.data.bitcoinotc import BitcoinOTC
+from dgl.data.gdelt import GDELT
+from dgl.data.gindt import GINDataset
+from dgl.data.gnn_benchmark import AmazonCoBuy, Coauthor, CoraFull
+from dgl.data.icews18 import ICEWS18
+from dgl.data.karate import KarateClub
+from dgl.data.qm7b import QM7b
+from pytablewriter import MarkdownTableWriter, RstGridTableWriter
+
+ds_list = {
+    "BitcoinOTC": "BitcoinOTC()",
+    "Cora": "CitationGraphDataset('cora')",
+    "Citeseer": "CitationGraphDataset('citeseer')",
+    "PubMed": "CitationGraphDataset('pubmed')",
+    "QM7b": "QM7b()",
+    "Reddit": "RedditDataset()",
+    "ENZYMES": "TUDataset('ENZYMES')",
+    "DD": "TUDataset('DD')",
+    "COLLAB": "TUDataset('COLLAB')",
+    "MUTAG": "TUDataset('MUTAG')",
+    "PROTEINS": "TUDataset('PROTEINS')",
+    "PPI": "PPIDataset('train')/PPIDataset('valid')/PPIDataset('test')",
+    # "Cora Binary": "CitationGraphDataset('cora_binary')",
+    "KarateClub": "KarateClub()",
+    "Amazon computer": "AmazonCoBuy('computers')",
+    "Amazon photo": "AmazonCoBuy('photo')",
+    "Coauthor cs": "Coauthor('cs')",
+    "Coauthor physics": "Coauthor('physics')",
+    "GDELT": "GDELT('train')/GDELT('valid')/GDELT('test')",
+    "ICEWS18": "ICEWS18('train')/ICEWS18('valid')/ICEWS18('test')",
+    "CoraFull": "CoraFull()",
+}
+
+writer = RstGridTableWriter()
+# writer = MarkdownTableWriter()
+
+extract_graph = lambda g: g if isinstance(g, DGLGraph) else g[0]
+stat_list = []
+for k, v in ds_list.items():
+    print(k, " ", v)
+    ds = eval(v.split("/")[0])
+    num_nodes = []
+    num_edges = []
+    for i in range(len(ds)):
+        g = extract_graph(ds[i])
+        num_nodes.append(g.num_nodes())
+        num_edges.append(g.num_edges())
+
+    gg = extract_graph(ds[0])
+    dd = {
+        "Datset Name": k,
+        "Usage": v,
+        "# of graphs": len(ds),
+        "Avg. # of nodes": np.mean(num_nodes),
+        "Avg. # of edges": np.mean(num_edges),
+        "Node field": ", ".join(list(gg.ndata.keys())),
+        "Edge field": ", ".join(list(gg.edata.keys())),
+        # "Graph field": ', '.join(ds[0][0].gdata.keys()) if hasattr(ds[0][0], "gdata") else "",
+        "Temporal": hasattr(ds, "is_temporal"),
+    }
+    stat_list.append(dd)
+
+print(dd.keys())
+df = pd.DataFrame(stat_list)
+df = df.reindex(columns=dd.keys())
+writer.from_dataframe(df)
+
+writer.write_table()