Struct StochasticGraphLayer 

pub struct StochasticGraphLayer {
    pub n_nodes: usize,
    pub n_features: usize,
    pub storage: AdjStorage,
    pub weights: Vec<f64>,
    pub degrees: Vec<f64>,
}

Fields

n_nodes: usize

n_features: usize

storage: AdjStorage

weights: Vec<f64>

degrees: Vec<f64>

Implementations

impl StochasticGraphLayer

pub fn new( adj_flat: Vec<f64>, n_nodes: usize, n_features: usize, seed: u64, ) -> Self

Construct from dense adjacency matrix (backwards-compatible API).

pub fn new_sparse( csr: CsrMatrix, n_features: usize, seed: u64, ) -> Result<Self, String>

Construct from pre-built CSR adjacency.

pub fn from_dense_auto( adj_flat: Vec<f64>, n_nodes: usize, n_features: usize, seed: u64, density_threshold: f64, ) -> Self

Convert dense adjacency to CSR if density < density_threshold (default 0.3).

pub fn is_sparse(&self) -> bool

True if using sparse CSR storage.

fn validate_features(&self, node_features: &[f64]) -> Result<(), String>

fn aggregate_and_transform(&self, agg_flat: &[f64]) -> Vec<f64>

Aggregate + weight transform + tanh, dispatched on storage.

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

Rate-mode graph forward pass.

pub fn forward_sc( &self, node_features: &[f64], length: usize, seed: u64, ) -> Result<Vec<f64>, String>

SC-mode forward pass using AND+popcount message passing.

Dense: encodes full adjacency matrix. Sparse: encodes only non-zero CSR entries, avoiding O(n^2) dense expansion.

pub fn get_weights(&self) -> Vec<f64>

pub fn set_weights(&mut self, weights: Vec<f64>) -> Result<(), String>