TolokersDataset

class dgl.data.TolokersDataset(raw_dir=None, force_reload=False, verbose=True, transform=None)[source]

Bases: HeterophilousGraphDataset

Tolokers dataset from the β€˜A Critical Look at the Evaluation of GNNs under Heterophily: Are We Really Making Progress? <https://arxiv.org/abs/2302.11640>’__ paper.

This dataset is based on data from the Toloka crowdsourcing platform. The nodes represent tolokers (workers). An edge connects two tolokers if they have worked on the same task. The goal is to predict which tolokers have been banned in one of the projects. Node features are based on the worker’s profile information and task performance statistics.

Statistics:

  • Nodes: 11758

  • Edges: 1038000

  • Classes: 2

  • Node features: 10

  • 10 train/val/test splits

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 DGLGraph object and returns a transformed version. The DGLGraph object will be transformed before every access. Default: None

num_classes

Number of node classes

Type:

int

Examples

>>> from dgl.data import TolokersDataset
>>> dataset = TolokersDataset()
>>> g = dataset[0]
>>> num_classes = dataset.num_classes

>>> # get node features
>>> feat = g.ndata["feat"]

>>> # get the first data split
>>> train_mask = g.ndata["train_mask"][:, 0]
>>> val_mask = g.ndata["val_mask"][:, 0]
>>> test_mask = g.ndata["test_mask"][:, 0]

>>> # get labels
>>> label = g.ndata['label']
__getitem__(idx)

Gets the data object at index.

__len__()

The number of examples in the dataset.