Source code for torch_geometric.datasets.malnet_tiny

import glob
import os
import os.path as osp
from typing import Callable, List, Optional

import torch

from torch_geometric.data import (
    Data,
    InMemoryDataset,
    download_url,
    extract_tar,
)
from torch_geometric.utils import remove_isolated_nodes


[docs]class MalNetTiny(InMemoryDataset): r"""The MalNet Tiny dataset from the `"A Large-Scale Database for Graph Representation Learning" <https://openreview.net/pdf?id=1xDTDk3XPW>`_ paper. :class:`MalNetTiny` contains 5,000 malicious and benign software function call graphs across 5 different types. Each graph contains at most 5k nodes. Args: root (string): Root directory where the dataset should be saved. 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`) pre_filter (callable, optional): A function that takes in an :obj:`torch_geometric.data.Data` object and returns a boolean value, indicating whether the data object should be included in the final dataset. (default: :obj:`None`) """ url = 'http://malnet.cc.gatech.edu/graph-data/malnet-graphs-tiny.tar.gz' def __init__(self, root: str, transform: Optional[Callable] = None, pre_transform: Optional[Callable] = None, pre_filter: Optional[Callable] = None): super().__init__(root, transform, pre_transform, pre_filter) self.data, self.slices = torch.load(self.processed_paths[0]) @property def raw_file_names(self) -> List[str]: folders = ['addisplay', 'adware', 'benign', 'downloader', 'trojan'] return [osp.join('malnet-graphs-tiny', folder) for folder in folders] @property def processed_file_names(self) -> str: return 'data.pt' def download(self): path = download_url(self.url, self.raw_dir) extract_tar(path, self.raw_dir) os.unlink(path) def process(self): data_list = [] for y, raw_path in enumerate(self.raw_paths): raw_path = osp.join(raw_path, os.listdir(raw_path)[0]) filenames = glob.glob(osp.join(raw_path, '*.edgelist')) for filename in filenames: with open(filename, 'r') as f: edges = f.read().split('\n')[5:-1] edge_index = [[int(s) for s in edge.split()] for edge in edges] edge_index = torch.tensor(edge_index).t().contiguous() # Remove isolated nodes, including those with only a self-loop edge_index = remove_isolated_nodes(edge_index)[0] num_nodes = int(edge_index.max()) + 1 data = Data(edge_index=edge_index, y=y, num_nodes=num_nodes) data_list.append(data) if self.pre_filter is not None: data_list = [data for data in data_list if self.pre_filter(data)] if self.pre_transform is not None: data_list = [self.pre_transform(data) for data in data_list] torch.save(self.collate(data_list), self.processed_paths[0])