GravNet

Model variant implementing the GravNet architecture for graph-level prediction. Each block learns a low-dimensional latent space, connects each node to its nearest neighbors in that space, and aggregates features over those learned neighbors. Final node features are mean-pooled per graph and projected to the output width.

See: Learning representations of irregular particle-detector geometry with distance-weighted graph networks

Configuration

Selected as name: gravnet.

Option	Description	Type	Default
hidden_layers	Number of GravNet blocks.	int	required
hidden_channels	Width of each hidden layer.	int	required
num_neighbors	Number of nearest neighbors aggregated per node.	int	required
space_dimensions	Dimensionality of the learned latent space used for neighbor search.	int	required
propagate_dimensions	Dimensionality of the features propagated between neighbors.	int	required

components:
  model:
    name: gravnet
    kwargs:
      hidden_layers: 4
      hidden_channels: 256
      num_neighbors: 8
      space_dimensions: 4
      propagate_dimensions: 22