gammagl.layers.conv.GINConv

class GINConv(nn, eps=0.0, train_eps: bool = False, **kwargs)[source]

The graph isomorphism operator from the “How Powerful are Graph Neural Networks?” paper

\[\mathbf{x}^{\prime}_i = h_{\mathbf{\Theta}} \left( (1 + \epsilon) \cdot \mathbf{x}_i + \sum_{j \in \mathcal{N}(i)} \mathbf{x}_j \right)\]

or

\[\mathbf{X}^{\prime} = h_{\mathbf{\Theta}} \left( \left( \mathbf{A} + (1 + \epsilon) \cdot \mathbf{I} \right) \cdot \mathbf{X} \right),\]

here \(h_{\mathbf{\Theta}}\) denotes a neural network, .i.e. an MLP.

Parameters:

  • nn (tlx.nn.Module) – A neural network \(h_{\mathbf{\Theta}}\) that maps node features x of shape [-1, in_channels] to shape [-1, out_channels], e.g., defined by torch.nn.Sequential.

  • eps (float, optional) – (Initial) \(\epsilon\)-value. (default: 0.)

  • train_eps (bool, optional) – If set to True, \(\epsilon\) will be a trainable parameter. (default: False)

  • **kwargs (optional) – Additional arguments of gammagl.layers.conv.MessagePassing.

forward(x, edge_index, size=None)[source]
message(x, edge_index)[source]

Function that construct message from source nodes to destination nodes.

Parameters:

  • x (tensor) – input node feature.

  • edge_index (tensor) – edges from src to dst.

  • edge_weight (tensor, optional) – weight of each edge.

Returns:

  • tensor – output message

  • Returns – the message matrix, and the shape is [num_edges, message_dim]