"""
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
<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()
[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