dmlc--dgl
Da Zheng
a0721405cf
* fix dgl data. * remove more. * fix. * fix. Co-authored-by: Ubuntu <ubuntu@ip-172-31-16-150.us-west-2.compute.internal>
67 行
2.4 KiB
Python
67 行
2.4 KiB
Python
from scipy import io
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import numpy as np
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import os
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import datetime
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from .utils import get_download_dir, download, extract_archive
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from ..graph import DGLGraph
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class BitcoinOTC(object):
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"""
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This is who-trusts-whom network of people who trade using Bitcoin
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on a platform called Bitcoin OTC.
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Since Bitcoin users are anonymous, there is a need to maintain a
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record of users' reputation to prevent transactions with fraudulent
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and risky users. Members of Bitcoin OTC rate other members in a
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scale of -10 (total distrust) to +10 (total trust) in steps of 1.
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Reference:
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- `Bitcoin OTC trust weighted signed network <http://snap.stanford.edu/data/soc-sign-bitcoin-otc.html>`_
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- `EvolveGCN: Evolving Graph
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Convolutional Networks for Dynamic Graphs
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<https://arxiv.org/abs/1902.10191>`_
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"""
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_url = 'https://snap.stanford.edu/data/soc-sign-bitcoinotc.csv.gz'
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def __init__(self):
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self.dir = get_download_dir()
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self.zip_path = os.path.join(
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self.dir, 'bitcoin', "soc-sign-bitcoinotc.csv.gz")
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download(self._url, path=self.zip_path)
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extract_archive(self.zip_path, os.path.join(
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self.dir, 'bitcoin'))
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self.path = os.path.join(
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self.dir, 'bitcoin', "soc-sign-bitcoinotc.csv")
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self.graphs = []
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self._load(self.path)
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def _load(self, filename):
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data = np.loadtxt(filename, delimiter=',').astype(np.int64)
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data[:, 0:2] = data[:, 0:2] - data[:, 0:2].min()
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num_nodes = data[:, 0:2].max() - data[:, 0:2].min() + 1
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delta = datetime.timedelta(days=14).total_seconds()
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# The source code is not released, but the paper indicates there're
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# totally 137 samples. The cutoff below has exactly 137 samples.
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time_index = np.around(
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(data[:, 3] - data[:, 3].min())/delta).astype(np.int64)
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for i in range(time_index.max()):
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g = DGLGraph()
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g.add_nodes(num_nodes)
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row_mask = time_index <= i
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edges = data[row_mask][:, 0:2]
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rate = data[row_mask][:, 2]
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g.add_edges(edges[:, 0], edges[:, 1])
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g.edata['h'] = rate.reshape(-1, 1)
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self.graphs.append(g)
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def __getitem__(self, idx):
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return self.graphs[idx]
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def __len__(self):
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return len(self.graphs)
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@property
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def is_temporal(self):
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return True
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