Source code for

from scipy import io
import numpy as np
from dgl import DGLGraph
import os
import datetime

from .utils import get_download_dir, download, extract_archive

[docs]class BitcoinOTC(object): """ This is who-trusts-whom network of people who trade using Bitcoin on a platform called Bitcoin OTC. Since Bitcoin users are anonymous, there is a need to maintain a record of users' reputation to prevent transactions with fraudulent and risky users. Members of Bitcoin OTC rate other members in a scale of -10 (total distrust) to +10 (total trust) in steps of 1. Reference: - `Bitcoin OTC trust weighted signed network <>`_ - `EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs <>`_ """ _url = '' def __init__(self): self.dir = get_download_dir() self.zip_path = os.path.join( self.dir, 'bitcoin', "soc-sign-bitcoinotc.csv.gz") download(self._url, path=self.zip_path) extract_archive(self.zip_path, os.path.join( self.dir, 'bitcoin')) self.path = os.path.join( self.dir, 'bitcoin', "soc-sign-bitcoinotc.csv") self.graphs = [] self._load(self.path) def _load(self, filename): data = np.loadtxt(filename, delimiter=',').astype(np.int64) data[:, 0:2] = data[:, 0:2] - data[:, 0:2].min() num_nodes = data[:, 0:2].max() - data[:, 0:2].min() + 1 delta = datetime.timedelta(days=14).total_seconds() # The source code is not released, but the paper indicates there're # totally 137 samples. The cutoff below has exactly 137 samples. time_index = np.around( (data[:, 3] - data[:, 3].min())/delta).astype(np.int64) for i in range(time_index.max()): g = DGLGraph() g.add_nodes(num_nodes) row_mask = time_index <= i edges = data[row_mask][:, 0:2] rate = data[row_mask][:, 2] g.add_edges(edges[:, 0], edges[:, 1]) g.edata['h'] = rate.reshape(-1, 1) self.graphs.append(g) def __getitem__(self, idx): return self.graphs[idx] def __len__(self): return len(self.graphs) @property def is_temporal(self): return True