Struct EdgeConv 

pub struct EdgeConv { /* private fields */ }

Edge convolution layer (Wang et al., 2019).

Dynamically constructs graph based on k-nearest neighbors in feature space:

h_i' = max_{j ∈ N(i)} MLP(CONCAT(h_i, h_j - h_i))

Implementations

impl EdgeConv

pub fn new( in_features: usize, hidden_features: usize, out_features: usize, ) -> EdgeConv

Create new EdgeConv layer.

pub fn in_features(&self) -> usize

Get input feature dimension.

pub fn out_features(&self) -> usize

Get output feature dimension.

Trait Implementations

impl Debug for EdgeConv

fn fmt(&self, f: &mut Formatter<'_>) -> Result<(), Error>

Formats the value using the given formatter.

impl GNNModule for EdgeConv

fn forward_gnn(&self, x: &Tensor, edge_index: &[(usize, usize)]) -> Tensor

Forward pass with graph structure.

impl Module for EdgeConv

fn forward(&self, _input: &Tensor) -> Tensor

Perform forward computation.

fn parameters(&self) -> Vec<&Tensor>

Get references to all learnable parameters.

fn parameters_mut(&mut self) -> Vec<&mut Tensor>

Get mutable references to all learnable parameters.

fn refresh_caches(&mut self)

Refresh any cached computations after parameters have been modified.

fn train(&mut self)

Set the module to training mode.

fn eval(&mut self)

Set the module to evaluation mode.

fn training(&self) -> bool

Check if the module is in training mode.

fn zero_grad(&mut self)

Zero out gradients for all parameters.

fn num_parameters(&self) -> usize

Get the number of learnable parameters.