pydgn.model

model.dgn

• dgn.toy_dgn
• dgn.toy_mlp

model.readout

• readout.graph_readout
• readout.link_readout
• readout.node_readout

model.interface

class pydgn.model.interface.ModelInterface(*args: Any, **kwargs: Any)

Bases: torch.nn.Module

Provides the signature for any main model to be trained under PyDGN

Parameters

• dim_node_features (int) – dimension of node features (according to the DatasetInterface property)

• dim_edge_features (int) – dimension of edge features (according to the DatasetInterface property)

• dim_target (int) – dimension of the target (according to the DatasetInterface property)

• readout_class (Callable[…,:class:torch.nn.Module]) – class of the module implementing the readout. This is optional, but useful to put different readouts to try in the config file

• config (dict) – config dictionary containing all the necessary hyper-parameters plus additional information (if needed)

forward(data: torch_geometric.data.Batch) → Tuple[torch.Tensor, Optional[torch.Tensor], Optional[List[object]]]

Performs a forward pass over a batch of graphs

Parameters

data (torch_geometric.data.Batch) – a batch of graphs

Returns

a tuple (model’s output, [optional] node embeddings, [optional] additional outputs

class pydgn.model.interface.ReadoutInterface(*args: Any, **kwargs: Any)

Bases: torch.nn.Module

Provides the signature for any readout to be trained under PyDGN

Parameters

• dim_node_features (int) – dimension of node features (according to the DatasetInterface property)

• dim_edge_features (int) – dimension of edge features (according to the DatasetInterface property)

• dim_target (int) – dimension of the target (according to the DatasetInterface property)

• config (dict) – config dictionary containing all the necessary hyper-parameters plus additional information (if needed)

forward(node_embeddings: torch.tensor, batch: torch.Tensor, **kwargs) → Tuple[torch.Tensor, Optional[torch.Tensor], Optional[List[object]]]

Performs a forward pass over a batch of graphs

Parameters

• node_embeddings (torch_geometric.data.Batch) – the node embeddings

• batch (torch.Tensor) – the usual batch object of PyG

• kwargs (dict) – additional and optional arguments

Returns

a tuple (model’s output, [optional] node embeddings, [optional] additional outputs