from typing import Optional
from functools import partial
from dhg.datapipe import load_from_pickle, norm_ft, to_tensor, to_long_tensor, to_bool_tensor
from .base import BaseData
[docs]class TencentBiGraph(BaseData):
r"""The TencentBiGraph dataset is a social network dataset for vertex classification task.
This is a large-scale real-world social network represented by a bipartite graph.
Nodes in set :math:`U` are social network users, and nodes in set :math:`V` are social communities
(e.g., a subset of social network users who share the same interests in electrical products may
join the same shopping community). Both users and communities are described by dense off-the-shelf feature vectors.
The edge connection between two sets indicates that the user belongs to the community.
Note that this dataset provides classification labels for research purposes.
In real-world applications, labeling every node is impractical.
More details see the `Cascade-BGNN: Toward Efficient Self-supervised Representation Learning on Large-scale Bipartite Graphs <https://arxiv.org/pdf/1906.11994.pdf>`_ paper.
The content of the TencentBiGraph dataset includes the following:
- ``num_u_classes``: The number of classes in set :math:`U` : :math:`2`.
- ``num_u_vertices``: The number of vertices in set :math:`U` : :math:`619,030`.
- ``num_v_vertices``: The number of vertices in set :math:`V` : :math:`90,044`.
- ``num_edges``: The number of edges: :math:`144,501`.
- ``dim_u_features``: The dimension of features in set :math:`U` : :math:`8`.
- ``dim_v_features``: The dimension of features in set :math:`V` : :math:`16`.
- ``u_features``: The vertex feature matrix in set :math:`U`. ``torch.Tensor`` with size :math:`(619,030 \times 8)`.
- ``v_features``: The vertex feature matrix in set :math:`V` . ``torch.Tensor`` with size :math:`(90,044 \times 16)`.
- ``edge_list``: The edge list. ``List`` with length :math:`(991,713 \times 2)`.
- ``u_labels``: The label list in set :math:`U` . ``torch.LongTensor`` with size :math:`(619,030, )`.
Args:
``data_root`` (``str``, optional): The ``data_root`` has stored the data. If set to ``None``, this function will auto-download from server and save into the default direction ``~/.dhg/datasets/``. Defaults to ``None``.
"""
def __init__(self, data_root: Optional[str] = None) -> None:
super().__init__("tencent_bigraph", data_root)
self._content = {
"num_u_classes": 2,
"num_u_vertices": 619030,
"num_v_vertices": 90044,
"num_edges": 991713,
"dim_u_features": 8,
"dim_v_features": 16,
"u_features": {
"upon": [{"filename": "u_features.pkl", "md5": "f6984204d7c9953894678c16670fc7a6"}],
"loader": load_from_pickle,
"preprocess": [to_tensor, partial(norm_ft, ord=1)],
},
"v_features": {
"upon": [{"filename": "v_features.pkl", "md5": "5344dadb0b7274c26fc60ddc333e2939"}],
"loader": load_from_pickle,
"preprocess": [to_tensor, partial(norm_ft, ord=1)],
},
"edge_list": {
"upon": [{"filename": "edge_list.pkl", "md5": "f5f8a8478133e1c25c01aa09b7eb96a2"}],
"loader": load_from_pickle,
},
"u_labels": {
"upon": [{"filename": "u_labels.pkl", "md5": "1e4a226acb8b50589ed59623069d3887"}],
"loader": load_from_pickle,
"preprocess": [to_long_tensor],
},
}