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import os.path as osp
from typing import Callable, Optional
import numpy as np
import torch
from torch_geometric.data import Data, InMemoryDataset, extract_zip
from torch_geometric.utils import index_to_mask
class DGraphFin(InMemoryDataset):
r"""The DGraphFin networks from the
`"DGraph: A Large-Scale Financial Dataset for Graph Anomaly Detection"
<https://arxiv.org/abs/2207.03579>`_ paper.
It is a directed, unweighted dynamic graph consisting of millions of
nodes and edges, representing a realistic user-to-user social network
in financial industry.
Node represents a Finvolution user, and an edge from one
user to another means that the user regards the other user
as the emergency contact person. Each edge is associated with a
timestamp ranging from 1 to 821 and a type of emergency contact
ranging from 0 to 11.
Args:
root (str): 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`)
force_reload (bool, optional): Whether to re-process the dataset.
(default: :obj:`False`)
**STATS:**
.. list-table::
:widths: 10 10 10 10
:header-rows: 1
* - #nodes
- #edges
- #features
- #classes
* - 3,700,550
- 4,300,999
- 17
- 2
"""
url = "https://dgraph.xinye.com"
def __init__(
self,
root: str,
transform: Optional[Callable] = None,
pre_transform: Optional[Callable] = None,
force_reload: bool = False,
) -> None:
super().__init__(root, transform, pre_transform,
force_reload=force_reload)
self.load(self.processed_paths[0])
def download(self) -> None:
raise RuntimeError(
f"Dataset not found. Please download '{self.raw_file_names}' from "
f"'{self.url}' and move it to '{self.raw_dir}'")
@property
def raw_file_names(self) -> str:
return 'DGraphFin.zip'
@property
def processed_file_names(self) -> str:
return 'data.pt'
@property
def num_classes(self) -> int:
return 2
def process(self) -> None:
extract_zip(self.raw_paths[0], self.raw_dir, log=False)
path = osp.join(self.raw_dir, "dgraphfin.npz")
with np.load(path) as loader:
x = torch.from_numpy(loader['x']).to(torch.float)
y = torch.from_numpy(loader['y']).to(torch.long)
edge_index = torch.from_numpy(loader['edge_index']).to(torch.long)
edge_type = torch.from_numpy(loader['edge_type']).to(torch.long)
edge_time = torch.from_numpy(loader['edge_timestamp']).to(
torch.long)
train_nodes = torch.from_numpy(loader['train_mask']).to(torch.long)
val_nodes = torch.from_numpy(loader['valid_mask']).to(torch.long)
test_nodes = torch.from_numpy(loader['test_mask']).to(torch.long)
train_mask = index_to_mask(train_nodes, size=x.size(0))
val_mask = index_to_mask(val_nodes, size=x.size(0))
test_mask = index_to_mask(test_nodes, size=x.size(0))
data = Data(x=x, edge_index=edge_index.t(), edge_type=edge_type,
edge_time=edge_time, y=y, train_mask=train_mask,
val_mask=val_mask, test_mask=test_mask)
data = data if self.pre_transform is None else self.pre_transform(data)
self.save([data], self.processed_paths[0])
|
