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dbgnn

DBGNN

Bases: torch.nn.Module

Implementation of time-aware graph neural network DBGNN (Reference paper).

Parameters:

Name Type Description Default
num_classes int

number of classes

 required
num_features list[int]

number of features for first order and higher order nodes, e.g. [first_order_num_features, second_order_num_features]

 required
hidden_dims list[int]

number of hidden dimensions per each layer in the first/higher order network

 required
p_dropout float

drop-out probability

 0.0
Source code in src/pathpyG/nn/dbgnn.py 
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class DBGNN(Module):
    """Implementation of time-aware graph neural network DBGNN ([Reference paper](https://openreview.net/pdf?id=Dbkqs1EhTr)).

    Args:
        num_classes: number of classes
        num_features: number of features for first order and higher order nodes, e.g. [first_order_num_features, second_order_num_features]
        hidden_dims: number of hidden dimensions per each layer in the first/higher order network
        p_dropout: drop-out probability
    """
    def __init__(
        self,
        num_classes: int,
        num_features: list[int],
        hidden_dims: list[int],
        p_dropout: float = 0.0
    ):
        super().__init__()

        self.num_features = num_features
        self.num_classes = num_classes
        self.hidden_dims = hidden_dims
        self.p_dropout = p_dropout

        # higher-order layers
        self.higher_order_layers = ModuleList()
        self.higher_order_layers.append(GCNConv(self.num_features[1], self.hidden_dims[0]))

        # first-order layers
        self.first_order_layers = ModuleList()
        self.first_order_layers.append(GCNConv(self.num_features[0], self.hidden_dims[0]))

        for dim in range(1, len(self.hidden_dims)-1):
            # higher-order layers
            self.higher_order_layers.append(GCNConv(self.hidden_dims[dim-1], self.hidden_dims[dim]))
            # first-order layers
            self.first_order_layers.append(GCNConv(self.hidden_dims[dim-1], self.hidden_dims[dim]))

        self.bipartite_layer = BipartiteGraphOperator(self.hidden_dims[-2], self.hidden_dims[-1])

        # Linear layer
        self.lin = torch.nn.Linear(self.hidden_dims[-1], num_classes)



    def forward(self, data):

        x = data.x
        x_h = data.x_h

        # First-order convolutions
        for layer in self.first_order_layers:
            x = F.dropout(x, p=self.p_dropout, training=self.training)
            x = F.elu(layer(x, data.edge_index, data.edge_weights))
        x = F.dropout(x, p=self.p_dropout, training=self.training)

        # Second-order convolutions
        for layer in self.higher_order_layers:
            x_h = F.dropout(x_h, p=self.p_dropout, training=self.training)
            x_h = F.elu(layer(x_h, data.edge_index_higher_order, data.edge_weights_higher_order))
        x_h = F.dropout(x_h, p=self.p_dropout, training=self.training)

        # Bipartite message passing
        x = torch.nn.functional.elu(self.bipartite_layer((x_h, x), data.bipartite_edge_index, N = data.num_ho_nodes, M= data.num_nodes))
        x = F.dropout(x, p=self.p_dropout, training=self.training)

        # Linear layer
        x = self.lin(x)

        return x