import os
import os.path
from ggfm.data import graph as gg
from typing import Callable, List, Optional
from ggfm.data import download_url, extract_zip
[docs]class MAG:
r"""
The Microsoft Academic Graph (MAG) is a comprehensive dataset that encompasses a vast collection of academic
publications, authors, conferences, journals, and citation relationships. It serves as a valuable resource for
research in bibliometrics, citation analysis, and academic network analysis.
The dataset is sourced from https://ogb.stanford.edu/docs/lsc/mag240m/, which provides the MAG240M version,
containing over 240 million citation links and other related information from academic papers across various domains.
"""
url = 'todo'
def __init__(self, root: str = None, transform: Optional[Callable] = None,
pre_transform: Optional[Callable] = None, force_reload: bool = False):
super().__init__(root, transform, pre_transform, force_reload=force_reload)
self.data, self.slices = self.load_data(self.processed_paths[0])
@property
def raw_file_names(self) -> List[str]:
pass
@property
def processed_file_names(self) -> str:
return 'mag_pre_data.pt'
def download(self):
path = download_url(self.url, self.raw_dir)
extract_zip(path, self.raw_dir)
os.remove(path)
def process(self):
data = gg()
pass
# todo
def __repr__(self) -> str:
return f'{self.__class__.__name__}()'