Source code for

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

[docs]class ActorDataset(DGLBuiltinDataset): r"""Actor-only induced subgraph of the film-directoractor-writer network from `Social Influence Analysis in Large-scale Networks <>`, introduced by `Geom-GCN: Geometric Graph Convolutional Networks <>` 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/"), 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"\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 ="\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}/{}_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()
[docs] 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)
[docs] def __len__(self): return 1
@property def num_classes(self): return self._num_classes