easygraph.model.hypergraphs.unignn module#
- class easygraph.model.hypergraphs.unignn.UniGAT(*args: Any, **kwargs: Any)[source]#
Bases:
Module
The UniGAT model proposed in UniGNN: a Unified Framework for Graph and Hypergraph Neural Networks paper (IJCAI 2021).
- Parameters:
in_channels (
int
) – \(C_{in}\) is the number of input channels.hid_channels (
int
) – \(C_{hid}\) is the number of hidden channels.num_classes (
int
) – The Number of class of the classification task.num_heads (
int
) – The Number of attention head in each layer.use_bn (
bool
) – If set toTrue
, use batch normalization. Defaults toFalse
.drop_rate (
float
) – The dropout probability. Defaults to0.5
.atten_neg_slope (
float
) – Hyper-parameter of theLeakyReLU
activation of edge attention. Defaults to 0.2.
- class easygraph.model.hypergraphs.unignn.UniGCN(*args: Any, **kwargs: Any)[source]#
Bases:
Module
The UniGCN model proposed in UniGNN: a Unified Framework for Graph and Hypergraph Neural Networks paper (IJCAI 2021).
- Parameters:
in_channels (
int
) – \(C_{in}\) is the number of input channels.hid_channels (
int
) – \(C_{hid}\) is the number of hidden channels.num_classes (
int
) – The Number of class of the classification task.use_bn (
bool
) – If set toTrue
, use batch normalization. Defaults toFalse
.drop_rate (
float
, optional) – Dropout ratio. Defaults to0.5
.
- class easygraph.model.hypergraphs.unignn.UniGIN(*args: Any, **kwargs: Any)[source]#
Bases:
Module
The UniGIN model proposed in UniGNN: a Unified Framework for Graph and Hypergraph Neural Networks paper (IJCAI 2021).
- Parameters:
in_channels (
int
) – \(C_{in}\) is the number of input channels.hid_channels (
int
) – \(C_{hid}\) is the number of hidden channels.num_classes (
int
) – The Number of class of the classification task.eps (
float
) – The epsilon value. Defaults to0.0
.train_eps (
bool
) – If set toTrue
, the epsilon value will be trainable. Defaults toFalse
.use_bn (
bool
) – If set toTrue
, use batch normalization. Defaults toFalse
.drop_rate (
float
, optional) – Dropout ratio. Defaults to0.5
.
- class easygraph.model.hypergraphs.unignn.UniSAGE(*args: Any, **kwargs: Any)[source]#
Bases:
Module
The UniSAGE model proposed in UniGNN: a Unified Framework for Graph and Hypergraph Neural Networks paper (IJCAI 2021).
- Parameters:
in_channels (
int
) – \(C_{in}\) is the number of input channels.hid_channels (
int
) – \(C_{hid}\) is the number of hidden channels.num_classes (
int
) – The Number of class of the classification task.use_bn (
bool
) – If set toTrue
, use batch normalization. Defaults toFalse
.drop_rate (
float
, optional) – Dropout ratio. Defaults to0.5
.