Struct GATLayer 

pub struct GATLayer {
    pub weights: Vec<Vec<f64>>,
    pub attention_weights: Vec<f64>,
    pub out_dim: usize,
    pub negative_slope: f64,
    pub use_activation: bool,
}

Graph Attention Network layer - legacy Vec API

Fields

weights: Vec<Vec<f64>>

Node transformation matrix W (out_dim × in_dim)

attention_weights: Vec<f64>

Attention vector a (2 * out_dim)

out_dim: usize

Output dimension

negative_slope: f64

LeakyReLU negative slope

use_activation: bool

Whether to apply ELU activation on output

Implementations

impl GATLayer

pub fn new(in_dim: usize, out_dim: usize) -> Self

Create a new GAT layer

Trait Implementations

impl Clone for GATLayer

fn clone(&self) -> GATLayer

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for GATLayer

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

Formats the value using the given formatter.

impl MessagePassingLayer for GATLayer

fn aggregate( &self, node_features: &[Vec<f64>], adjacency: &[(usize, usize, f64)], n_nodes: usize, ) -> Result<Vec<Vec<f64>>>

Aggregate messages from the neighborhood of each node

fn update( &self, aggregated: &[Vec<f64>], _node_features: &[Vec<f64>], ) -> Result<Vec<Vec<f64>>>

Update node embeddings using aggregated messages and self-features

fn forward( &self, node_features: &[Vec<f64>], adjacency: &[(usize, usize, f64)], ) -> Result<Vec<Vec<f64>>>

Run one full forward pass: aggregate + update