chore: import upstream snapshot with attribution

2026-07-13 12:36:30 +08:00
commit 55ab4e4a73
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+"""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
+
+
+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,
+        )
+
+    def download(self):
+        archive = os.path.join(self.raw_dir, self.name + ".zip")
+        download(self.url, path=archive)
+        extract_archive(archive, self.raw_path)
+
+    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