easygraph.datasets.gnn_benchmark module#
- class easygraph.datasets.gnn_benchmark.AmazonCoBuyComputerDataset(raw_dir=None, force_reload=False, verbose=True, transform=None)[source]#
Bases:
GNNBenchmarkDataset
‘Computer’ part of the 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: shchur/gnn-benchmark
Statistics:
Nodes: 13,752
Edges: 491,722 (note that the original dataset has 245,778 edges but DGL adds the reverse edges and remove the duplicates, hence with a different number)
Number of classes: 10
Node feature size: 767
- Parameters:
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. TheDGLGraph
object will be transformed before every access.
- num_classes#
Number of classes for each node.
- Type:
int
Examples
>>> data = AmazonCoBuyComputerDataset() >>> g = data[0] >>> num_class = data.num_classes >>> feat = g.ndata['feat'] # get node feature >>> label = g.ndata['label'] # get node labels
- property num_classes#
Number of classes.
- Return type:
int