import datetime
import os
from typing import Callable, Optional
import torch
from torch_geometric.data import (
Data,
InMemoryDataset,
download_url,
extract_gz,
)
[docs]class BitcoinOTC(InMemoryDataset):
r"""The Bitcoin-OTC dataset from the `"EvolveGCN: Evolving Graph
Convolutional Networks for Dynamic Graphs"
<https://arxiv.org/abs/1902.10191>`_ paper, consisting of 138
who-trusts-whom networks of sequential time steps.
Args:
root (str): Root directory where the dataset should be saved.
edge_window_size (int, optional): The window size for the existence of
an edge in the graph sequence since its initial creation.
(default: :obj:`10`)
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`)
force_reload (bool, optional): Whether to re-process the dataset.
(default: :obj:`False`)
**STATS:**
.. list-table::
:widths: 10 10 10 10 10
:header-rows: 1
* - #graphs
- #nodes
- #edges
- #features
- #classes
* - 138
- 6,005
- ~2,573.2
- 0
- 0
"""
url = 'https://snap.stanford.edu/data/soc-sign-bitcoinotc.csv.gz'
def __init__(
self,
root: str,
edge_window_size: int = 10,
transform: Optional[Callable] = None,
pre_transform: Optional[Callable] = None,
force_reload: bool = False,
) -> None:
self.edge_window_size = edge_window_size
super().__init__(root, transform, pre_transform,
force_reload=force_reload)
self.load(self.processed_paths[0])
@property
def raw_file_names(self) -> str:
return 'soc-sign-bitcoinotc.csv'
@property
def processed_file_names(self) -> str:
return 'data.pt'
@property
def num_nodes(self) -> int:
assert isinstance(self._data, Data)
assert self._data.edge_index is not None
return int(self._data.edge_index.max()) + 1
def download(self) -> None:
path = download_url(self.url, self.raw_dir)
extract_gz(path, self.raw_dir)
os.unlink(path)
def process(self) -> None:
with open(self.raw_paths[0], 'r') as f:
lines = [[x for x in line.split(',')]
for line in f.read().split('\n')[:-1]]
edge_indices = [[int(line[0]), int(line[1])] for line in lines]
edge_index = torch.tensor(edge_indices, dtype=torch.long)
edge_index = edge_index - edge_index.min()
edge_index = edge_index.t().contiguous()
num_nodes = int(edge_index.max()) + 1
edge_attrs = [int(line[2]) for line in lines]
edge_attr = torch.tensor(edge_attrs, dtype=torch.long)
stamps = [
datetime.datetime.fromtimestamp(int(float(line[3])))
for line in lines
]
offset = datetime.timedelta(days=13.8) # Results in 138 time steps.
graph_indices, factor = [], 1
for t in stamps:
factor = factor if t < stamps[0] + factor * offset else factor + 1
graph_indices.append(factor - 1)
graph_idx = torch.tensor(graph_indices, dtype=torch.long)
data_list = []
for i in range(int(graph_idx.max()) + 1):
mask = (graph_idx > (i - self.edge_window_size)) & (graph_idx <= i)
data = Data()
data.edge_index = edge_index[:, mask]
data.edge_attr = edge_attr[mask]
data.num_nodes = num_nodes
data_list.append(data)
if self.pre_filter is not None:
data_list = [d for d in data_list if self.pre_filter(d)]
if self.pre_transform is not None:
data_list = [self.pre_transform(d) for d in data_list]
self.save(data_list, self.processed_paths[0])