AmazonCoBuyComputerDataset

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

Bases: GNNBenchmarkDataset

β€˜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: https://github.com/shchur/gnn-benchmark#datasets

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. The DGLGraph 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
__getitem__(idx)

Get graph by index

Parameters:

idx (int) – Item index

Returns:

The graph contains:

  • ndata['feat']: node features

  • ndata['label']: node labels

Return type:

dgl.DGLGraph

__len__()

Number of graphs in the dataset