import torch
from torch_geometric.data import InMemoryDataset, download_url
from torch_geometric.io import read_npz
[docs]class Coauthor(InMemoryDataset):
r"""The Coauthor CS and Coauthor Physics networks from the
`"Pitfalls of Graph Neural Network Evaluation"
<https://arxiv.org/abs/1811.05868>`_ paper.
Nodes represent authors that are connected by an edge if they co-authored a
paper.
Given paper keywords for each author's papers, the task is to map authors
to their respective field of study.
Args:
root (string): Root directory where the dataset should be saved.
name (string): The name of the dataset (:obj:`"CS"`,
:obj:`"Physics"`).
transform (callable, optional): A function/transform that takes in an
:obj:`torch_geometric.data.Data` object and returns a transformed
version. The data object will be transformed before every access.
(default: :obj:`None`)
pre_transform (callable, optional): A function/transform that takes in
an :obj:`torch_geometric.data.Data` object and returns a
transformed version. The data object will be transformed before
being saved to disk. (default: :obj:`None`)
"""
url = 'https://github.com/shchur/gnn-benchmark/raw/master/data/npz/'
def __init__(self, root, name, transform=None, pre_transform=None):
assert name.lower() in ['cs', 'physics']
self.name = 'CS' if name.lower() == 'cs' else 'Physics'
super(Coauthor, self).__init__(root, transform, pre_transform)
self.data, self.slices = torch.load(self.processed_paths[0])
@property
def raw_file_names(self):
return 'ms_academic_{}.npz'.format(self.name[:3].lower())
@property
def processed_file_names(self):
return 'data.pt'
def download(self):
download_url(self.url + self.raw_file_names, self.raw_dir)
def process(self):
data = read_npz(self.raw_paths[0])
data = data if self.pre_transform is None else self.pre_transform(data)
data, slices = self.collate([data])
torch.save((data, slices), self.processed_paths[0])
def __repr__(self):
return '{}{}()'.format(self.__class__.__name__, self.name)