AmazonCoBuyPhotoDatasetΒΆ
-
class
dgl.data.
AmazonCoBuyPhotoDataset
(raw_dir=None, force_reload=False, verbose=False, transform=None)[source]ΒΆ Bases:
dgl.data.gnn_benchmark.GNNBenchmarkDataset
AmazonCoBuy dataset for node classification task.
Amazon Computers and Amazon Photo are segments of the Amazon co-purchase graph [McAuley et al., 2015], where nodes represent goods, edges indicate that two goods are frequently bought together, node features are bag-of-words encoded product reviews, and class labels are given by the product category.
Reference: https://github.com/shchur/gnn-benchmark#datasets
Statistics
Nodes: 7,650
Edges: 238,163 (note that the original dataset has 119,043 edges but DGL adds the reverse edges and remove the duplicates, hence with a different number)
Number of classes: 8
Node feature size: 745
- Parameters
raw_dir (str) β Raw file directory to download/contains the input data directory. Default: ~/.dgl/
force_reload (bool) β Whether to reload the dataset. Default: False
verbose (bool) β Whether to print out progress information. Default: True.
transform (callable, optional) β A transform that takes in a
DGLGraph
object and returns a transformed version. TheDGLGraph
object will be transformed before every access.
Examples
>>> data = AmazonCoBuyPhotoDataset() >>> g = data[0] >>> num_class = data.num_classes >>> feat = g.ndata['feat'] # get node feature >>> label = g.ndata['label'] # get node labels
-
__getitem__
(idx)ΒΆ Get graph by index
- Parameters
idx (int) β Item index
- Returns
The graph contains:
ndata['feat']
: node featuresndata['label']
: node labels
- Return type
-
__len__
()ΒΆ Number of graphs in the dataset