RedditDataset¶

class dgl.data.RedditDataset(self_loop=False, raw_dir=None, force_reload=False, verbose=False, transform=None)[source]¶

Bases: dgl.data.dgl_dataset.DGLBuiltinDataset

Reddit dataset for community detection (node classification)

Deprecated since version 0.5.0:

graph is deprecated, it is replaced by:

>>> dataset = RedditDataset()
>>> graph = dataset[0]

num_labels is deprecated, it is replaced by:

>>> dataset = RedditDataset()
>>> num_classes = dataset.num_classes

train_mask is deprecated, it is replaced by:

>>> dataset = RedditDataset()
>>> graph = dataset[0]
>>> train_mask = graph.ndata['train_mask']

val_mask is deprecated, it is replaced by:

>>> dataset = RedditDataset()
>>> graph = dataset[0]
>>> val_mask = graph.ndata['val_mask']

test_mask is deprecated, it is replaced by:

>>> dataset = RedditDataset()
>>> graph = dataset[0]
>>> test_mask = graph.ndata['test_mask']

features is deprecated, it is replaced by:

>>> dataset = RedditDataset()
>>> graph = dataset[0]
>>> features = graph.ndata['feat']

labels is deprecated, it is replaced by:

>>> dataset = RedditDataset()
>>> graph = dataset[0]
>>> labels = graph.ndata['label']

This is a graph dataset from Reddit posts made in the month of September, 2014. The node label in this case is the community, or “subreddit”, that a post belongs to. The authors sampled 50 large communities and built a post-to-post graph, connecting posts if the same user comments on both. In total this dataset contains 232,965 posts with an average degree of 492. We use the first 20 days for training and the remaining days for testing (with 30% used for validation).

Reference: http://snap.stanford.edu/graphsage/

Statistics

Nodes: 232,965
Edges: 114,615,892
Node feature size: 602
Number of training samples: 153,431
Number of validation samples: 23,831
Number of test samples: 55,703

Parameters

self_loop (bool) – Whether load dataset with self loop connections. Default: False
raw_dir (str) – Raw file directory to download/contains the input data directory. Default: ~/.dgl/
force_reload (bool) – Whether to reload the dataset. Default: False
verbose (bool) – Whether to print out 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.

num_classes¶

Number of classes for each node

Type: int

graph¶

Graph of the dataset

Type: dgl.DGLGraph

num_labels¶

Number of classes for each node

Type: int

train_mask¶

Mask of training nodes

Type: numpy.ndarray

val_mask¶

Mask of validation nodes

Type: numpy.ndarray

test_mask¶

Mask of test nodes

Type: numpy.ndarray

features¶

Node features

Type: Tensor

labels¶

Node labels

Type: Tensor

Examples

>>> data = RedditDataset()
>>> g = data[0]
>>> num_classes = data.num_classes
>>>
>>> # get node feature
>>> feat = g.ndata['feat']
>>>
>>> # get data split
>>> train_mask = g.ndata['train_mask']
>>> val_mask = g.ndata['val_mask']
>>> test_mask = g.ndata['test_mask']
>>>
>>> # get labels
>>> label = g.ndata['label']
>>>
>>> # Train, Validation and Test

__getitem__(idx)[source]¶

Get graph by index

Parameters

idx (int) – Item index

Returns

graph structure, node labels, node features and splitting masks:

ndata['label']: node label
ndata['feat']: node feature
ndata['train_mask']： mask for training node set
ndata['val_mask']: mask for validation node set
ndata['test_mask']: mask for test node set

Return type

dgl.DGLGraph

__len__()[source]¶: Number of graphs in the dataset