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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+"""
+Actor-only induced subgraph of the film-directoractor-writer network.
+"""
+import os
+
+import numpy as np
+
+from ..convert import graph
+from .dgl_dataset import DGLBuiltinDataset
+from .utils import _get_dgl_url
+
+
+class ActorDataset(DGLBuiltinDataset):
+    r"""Actor-only induced subgraph of the film-directoractor-writer network
+    from `Social Influence Analysis in Large-scale Networks
+    <https://dl.acm.org/doi/10.1145/1557019.1557108>`, introduced by
+    `Geom-GCN: Geometric Graph Convolutional Networks
+    <https://arxiv.org/abs/2002.05287>`
+
+    Nodes represent actors, and edges represent co-occurrence on the same
+    Wikipedia page. Node features correspond to some keywords in the Wikipedia
+    pages.
+
+    Statistics:
+
+    - Nodes: 7600
+    - Edges: 33391
+    - Number of Classes: 5
+    - 10 train/val/test splits
+
+        - Train: 3648
+        - Val: 2432
+        - Test: 1520
+
+    Parameters
+    ----------
+    raw_dir : str, optional
+        Raw file directory to store the processed data. Default: ~/.dgl/
+    force_reload : bool, optional
+        Whether to re-download the data source. Default: False
+    verbose : bool, optional
+        Whether to print progress information. Default: True
+    transform : callable, optional
+        A transform that takes in a :class:`~dgl.DGLGraph` object and returns
+        a transformed version. The :class:`~dgl.DGLGraph` object will be
+        transformed before every access. Default: None
+
+    Attributes
+    ----------
+    num_classes : int
+        Number of node classes
+
+    Notes
+    -----
+    The graph does not come with edges for both directions.
+    """
+
+    def __init__(
+        self, raw_dir=None, force_reload=False, verbose=True, transform=None
+    ):
+        super(ActorDataset, self).__init__(
+            name="actor",
+            url=_get_dgl_url("dataset/actor.zip"),
+            raw_dir=raw_dir,
+            force_reload=force_reload,
+            verbose=verbose,
+            transform=transform,
+        )
+
+    def process(self):
+        """Load and process the data."""
+        try:
+            import torch
+        except ImportError:
+            raise ModuleNotFoundError(
+                "This dataset requires PyTorch to be the backend."
+            )
+
+        # Process node features and labels.
+        with open(f"{self.raw_path}/out1_node_feature_label.txt", "r") as f:
+            data = [x.split("\t") for x in f.read().split("\n")[1:-1]]
+
+            rows, cols = [], []
+            labels = torch.empty(len(data), dtype=torch.long)
+            for n_id, col, label in data:
+                col = [int(x) for x in col.split(",")]
+                rows += [int(n_id)] * len(col)
+                cols += col
+
+                labels[int(n_id)] = int(label)
+
+            row, col = torch.tensor(rows), torch.tensor(cols)
+            features = torch.zeros(len(data), int(col.max()) + 1)
+            features[row, col] = 1.0
+
+            self._num_classes = int(labels.max().item()) + 1
+
+        # Process graph structure.
+        with open(f"{self.raw_path}/out1_graph_edges.txt", "r") as f:
+            data = f.read().split("\n")[1:-1]
+            data = [[int(v) for v in r.split("\t")] for r in data]
+        dst, src = torch.tensor(data, dtype=torch.long).t().contiguous()
+
+        self._g = graph((src, dst), num_nodes=features.size(0))
+        self._g.ndata["feat"] = features
+        self._g.ndata["label"] = labels
+
+        # Process 10 train/val/test node splits.
+        train_masks, val_masks, test_masks = [], [], []
+        for i in range(10):
+            filepath = f"{self.raw_path}/{self.name}_split_0.6_0.2_{i}.npz"
+            f = np.load(filepath)
+            train_masks += [torch.from_numpy(f["train_mask"])]
+            val_masks += [torch.from_numpy(f["val_mask"])]
+            test_masks += [torch.from_numpy(f["test_mask"])]
+        self._g.ndata["train_mask"] = torch.stack(train_masks, dim=1).bool()
+        self._g.ndata["val_mask"] = torch.stack(val_masks, dim=1).bool()
+        self._g.ndata["test_mask"] = torch.stack(test_masks, dim=1).bool()
+
+    def has_cache(self):
+        return os.path.exists(self.raw_path)
+
+    def load(self):
+        self.process()
+
+    def __getitem__(self, idx):
+        assert idx == 0, "This dataset has only one graph."
+        if self._transform is None:
+            return self._g
+        else:
+            return self._transform(self._g)
+
+    def __len__(self):
+        return 1
+
+    @property
+    def num_classes(self):
+        return self._num_classes