from typing import Union, Optional
from pathlib import Path
import os
import torch
from PlanetAlign.data import Dataset
from .utils import download_file_from_google_drive
[docs]
class ACM_DBLP(Dataset):
"""
A pair of undirected co-authorship networks, ACM and DBLP. Nodes represent authors and edges an edge exists between
two authors if they co-author at least one paper. Node attributes are available in both networks.
There are 6,325 common authors across two networks.
**STATS:**
.. list-table::
:widths: 10 10 10 10 10
:header-rows: 1
* - Graph
- #nodes
- #edges
- #node attrs
- #edge attrs
* - Phone
- 9,872
- 39,561
- 17
- 0
* - Email
- 9,916
- 44,808
- 17
- 0
"""
def __init__(self,
root: Union[str, Path],
download: Optional[bool] = False,
train_ratio: Optional[float] = 0.2,
dtype: torch.dtype = torch.float32,
seed: Optional[int] = 0):
if download:
download_file_from_google_drive(
remote_file_id='1-MecFHVRnlvqjmUbSGCizdvsedsiET98',
save_filename='ACM-DBLP.pt',
root=root)
if not self._check_integrity(root):
raise RuntimeError('ACM-DBLP dataset not found or corrupted. You can use download=True to download it')
super(ACM_DBLP, self).__init__(root=root, name='ACM-DBLP', train_ratio=train_ratio, dtype=dtype, seed=seed)
def _check_integrity(self, root):
return os.path.exists(os.path.join(root, 'ACM-DBLP.pt'))