GINE

Model variant implementing a graph isomorphism network with edge features (GINE) for graph-level prediction. Each layer adds a projection of the edge attributes to the neighbouring node features, sums the result over the neighbours of each node, adds the node’s own features scaled by 1 + eps, and passes the sum through a two layer MLP and an activation. The final node features are mean-pooled per graph and projected to the output width.

Configuration

Selected as name: gine.

Option	Description	Type	Default
hidden_layers	Number of GINE convolution layers.	int	required
hidden_channels	Width of each hidden layer.	int	required
edge_channels	Width of the edge attributes produced by the edge builder.	int	1
train_eps	Learn eps in each layer instead of fixing it at 0.	bool	false
checkpoint	Recompute each layer during the backward pass instead of storing its activations. Each layer stores a tensor of size edges by channels, so this cuts training memory by a large factor at the cost of roughly one extra forward pass. Only applies in training.	bool	false

components:
  model:
    name: gine
    kwargs:
      hidden_layers: 4
      hidden_channels: 256