easygraph.datasets.hypergraph.cocitation module#

class easygraph.datasets.hypergraph.cocitation.CocitationCiteseer(data_root: str | None = None)[source]#

Bases: BaseData

The Co-citation Citeseer dataset is a citation network dataset for vertex classification task. More details see the HyperGCN paper.

The content of the Co-citation Citaseer dataset includes the following:

num_classes: The number of classes: \(6\).
num_vertices: The number of vertices: \(3,327\).
num_edges: The number of edges: \(1,079\).
dim_features: The dimension of features: \(3,703\).
features: The vertex feature matrix. torch.Tensor with size \((3,327 \times 3,703)\).
edge_list: The edge list. List with length \(1,079\).
labels: The label list. torch.LongTensor with size \((3,327, )\).
train_mask: The train mask. torch.BoolTensor with size \((3,327, )\).
val_mask: The validation mask. torch.BoolTensor with size \((3,327, )\).
test_mask: The test mask. torch.BoolTensor with size \((3,327, )\).

Parameters:

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.

Attributes:

content: Return the content of the dataset.

Methods

`fetch_files`(files)	Download and check the files if they are not exist.
`needs_to_load`(item_name)	Return whether the `item_name` of the dataset needs to be loaded.
`raw`(key)	Return the `key` of the dataset with un-preprocessed format.

class easygraph.datasets.hypergraph.cocitation.CocitationCora(data_root: str | None = None)[source]#

Bases: BaseData

The Co-citation Cora dataset is a citation network dataset for vertex classification task. More details see the HyperGCN paper.

The content of the Co-citation Cora dataset includes the following:

num_classes: The number of classes: \(7\).
num_vertices: The number of vertices: \(2,708\).
num_edges: The number of edges: \(1,579\).
dim_features: The dimension of features: \(1,433\).
features: The vertex feature matrix. torch.Tensor with size \((2,708 \times 1,433)\).
edge_list: The edge list. List with length \(1,579\).
labels: The label list. torch.LongTensor with size \((2,708, )\).
train_mask: The train mask. torch.BoolTensor with size \((2,708, )\).
val_mask: The validation mask. torch.BoolTensor with size \((2,708, )\).
test_mask: The test mask. torch.BoolTensor with size \((2,708, )\).

Parameters:

Attributes:

content: Return the content of the dataset.

Methods

`fetch_files`(files)	Download and check the files if they are not exist.
`needs_to_load`(item_name)	Return whether the `item_name` of the dataset needs to be loaded.
`raw`(key)	Return the `key` of the dataset with un-preprocessed format.

class easygraph.datasets.hypergraph.cocitation.CocitationPubmed(data_root: str | None = None)[source]#

Bases: BaseData

The Co-citation PubMed dataset is a citation network dataset for vertex classification task. More details see the HyperGCN paper.

The content of the Co-citation PubMed dataset includes the following:

num_classes: The number of classes: \(3\).
num_vertices: The number of vertices: \(19,717\).
num_edges: The number of edges: \(7,963\).
dim_features: The dimension of features: \(500\).
features: The vertex feature matrix. torch.Tensor with size \((19,717 \times 500)\).
edge_list: The edge list. List with length \(7,963\).
labels: The label list. torch.LongTensor with size \((19,717, )\).
train_mask: The train mask. torch.BoolTensor with size \((19,717, )\).
val_mask: The validation mask. torch.BoolTensor with size \((19,717, )\).
test_mask: The test mask. torch.BoolTensor with size \((19,717, )\).

Parameters:

Attributes:

content: Return the content of the dataset.

Methods

`fetch_files`(files)	Download and check the files if they are not exist.
`needs_to_load`(item_name)	Return whether the `item_name` of the dataset needs to be loaded.
`raw`(key)	Return the `key` of the dataset with un-preprocessed format.