wehub-resource-sync
110 lines
3.7 KiB
Python
110 lines
3.7 KiB
Python
"""Facebook Ego-Net Dataset
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This dataset contains a subset of Facebook’s social network collected from
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survey participants in the SNAP EgoNet project. Nodes represent users, and
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edges indicate friendship links between them.
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Each ego network is centered on a user and includes their friend connections
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and friend-to-friend connections. The `.circles` files contain labeled groups
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(i.e., communities) of friends identified by the ego user.
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This version processes all ego-nets as a single undirected graph. Node features
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are not provided. Labels (circles) are optional and not included by default.
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Statistics (based on merged graph):
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- Nodes: ~4,000+
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- Edges: ~88,000+
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- Features: None
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- Classes: None
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Reference:
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J. McAuley and J. Leskovec, “Learning to Discover Social Circles in Ego Networks,”
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in NIPS, 2012. [https://snap.stanford.edu/data/egonets-Facebook.html]
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"""
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import os
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import easygraph as eg
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from easygraph.classes.graph import Graph
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from .graph_dataset_base import EasyGraphBuiltinDataset
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from .utils import download
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from .utils import extract_archive
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class FacebookEgoNetDataset(EasyGraphBuiltinDataset):
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r"""Facebook Ego-Net social network dataset.
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Each node is a user, and edges represent friendship. The dataset
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includes 10 ego networks centered on different users.
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Parameters
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----------
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raw_dir : str, optional
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Directory to store the raw downloaded files. Default: None
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force_reload : bool, optional
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Whether to re-download and process the dataset. Default: False
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verbose : bool, optional
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Whether to print detailed processing logs. Default: True
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transform : callable, optional
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Optional transform to apply on the graph.
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Examples
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--------
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>>> from easygraph.datasets import FacebookEgoNetDataset
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>>> dataset = FacebookEgoNetDataset()
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>>> g = dataset[0]
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>>> print("Nodes:", g.number_of_nodes())
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>>> print("Edges:", g.number_of_edges())
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"""
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def __init__(self, raw_dir=None, force_reload=False, verbose=True, transform=None):
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name = "facebook"
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url = "https://snap.stanford.edu/data/facebook.tar.gz"
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super(FacebookEgoNetDataset, self).__init__(
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name=name,
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url=url,
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raw_dir=raw_dir,
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force_reload=force_reload,
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verbose=verbose,
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transform=transform,
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)
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def process(self):
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parent_dir = os.path.join(self.raw_path, "facebook")
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g = eg.Graph()
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# Iterate over all .edges files in the subdirectory
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for filename in os.listdir(parent_dir):
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if filename.endswith(".edges"):
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edge_file = os.path.join(parent_dir, filename)
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with open(edge_file, "r") as f:
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for line in f:
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u, v = map(int, line.strip().split())
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g.add_edge(u, v)
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self._g = g
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self._num_nodes = g.number_of_nodes()
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self._num_edges = g.number_of_edges()
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if self.verbose:
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print("Finished loading Facebook Ego-Net dataset.")
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print(f" NumNodes: {self._num_nodes}")
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print(f" NumEdges: {self._num_edges}")
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def __getitem__(self, idx):
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assert idx == 0, "FacebookEgoNetDataset only contains one merged graph"
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return self._g if self._transform is None else self._transform(self._g)
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def __len__(self):
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return 1
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def download(self):
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r"""Automatically download data and extract it."""
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if self.url is not None:
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archive_path = os.path.join(self.raw_dir, self.name + ".tar.gz")
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download(self.url, path=archive_path)
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extract_archive(archive_path, self.raw_path)
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