"""GitHub Users Social Network Dataset (musae_git)
This dataset represents a directed social network of GitHub users collected in 2019.
Nodes represent GitHub developers, and a directed edge from user A to user B indicates that A follows B.
Each node also includes:
- Features: User profile and activity-based features.
- Labels: Developer's project area (e.g., machine learning, web dev, etc.)
Statistics:
- Nodes: 37,700
- Edges: 289,003
- Feature dim: 5,575
- Classes: 2
Reference:
J. Leskovec et al. "SNAP Datasets: Stanford Large Network Dataset Collection",
https://snap.stanford.edu/data/github-social.html
"""
import csv
import json
import os
import easygraph as eg
import numpy as np
from easygraph.classes.graph import Graph
from .graph_dataset_base import EasyGraphBuiltinDataset
from .utils import download
from .utils import extract_archive
[docs]
class GitHubUsersDataset(EasyGraphBuiltinDataset):
r"""GitHub developers social graph (musae_git).
Parameters
----------
raw_dir : str, optional
Directory to store raw data. Default: None
force_reload : bool, optional
Force re-download and processing. Default: False
verbose : bool, optional
Print processing information. Default: True
transform : callable, optional
Transform to apply to the graph on load.
Examples
--------
>>> from easygraph.datasets import GitHubUsersDataset
>>> dataset = GitHubUsersDataset()
>>> g = dataset[0]
>>> print("Nodes:", g.number_of_nodes())
>>> print("Edges:", g.number_of_edges())
>>> print("Feature shape:", g.nodes[0]['feat'].shape)
>>> print("Label:", g.nodes[0]['label'])
"""
def __init__(self, raw_dir=None, force_reload=False, verbose=True, transform=None):
name = "musae_git"
url = "https://snap.stanford.edu/data/git_web_ml.zip"
super(GitHubUsersDataset, self).__init__(
name=name,
url=url,
raw_dir=raw_dir,
force_reload=force_reload,
verbose=verbose,
transform=transform,
)
[docs]
def download(self):
archive = os.path.join(self.raw_dir, self.name + ".zip")
download(self.url, path=archive)
extract_archive(archive, self.raw_path)
[docs]
def process(self):
g = eg.DiGraph()
base_path = os.path.join(self.raw_path, "git_web_ml")
# Load node features
with open(os.path.join(base_path, "musae_git_features.json"), "r") as f:
features = json.load(f)
# Load labels
labels = {}
with open(os.path.join(base_path, "musae_git_target.csv"), "r") as f:
reader = csv.DictReader(f)
for row in reader:
node_id = int(row["id"])
labels[node_id] = int(row["ml_target"])
# Load edges
with open(os.path.join(base_path, "musae_git_edges.csv"), "r") as f:
reader = csv.DictReader(f)
for row in reader:
u, v = int(row["id_1"]), int(row["id_2"])
g.add_edge(u, v)
# Add node attributes
for node_id in g.nodes:
feat = np.array(features[str(node_id)], dtype=np.float32)
label = labels.get(node_id, -1)
g.add_node(node_id, feat=feat, label=label)
self._g = g
self._num_classes = len(set(labels.values()))
if self.verbose:
print("Finished loading GitHub Users dataset.")
print(f" NumNodes: {g.number_of_nodes()}")
print(f" NumEdges: {g.number_of_edges()}")
print(f" Feature dim: {feat.shape[0]}")
print(f" NumClasses: {self._num_classes}")
def __getitem__(self, idx):
assert idx == 0, "GitHubUsersDataset only contains one graph"
return self._g if self._transform is None else self._transform(self._g)
def __len__(self):
return 1
@property
def num_classes(self):
return self._num_classes
