Struct AdversarialGraphLearning 

pub struct AdversarialGraphLearning {
    pub k_neighbors: usize,
    pub robustness_lambda: f64,
    pub max_perturbation: f64,
    pub adversarial_steps: usize,
    pub adversarial_lr: f64,
    pub defense_strategy: String,
    pub consensus_threshold: f64,
    pub max_iter: usize,
    pub tolerance: f64,
    pub random_state: Option<u64>,
}

Adversarial graph learning for robust semi-supervised learning

Fields

k_neighbors: usize

Number of neighbors for graph construction

robustness_lambda: f64

Robustness parameter for adversarial defense

max_perturbation: f64

Maximum perturbation magnitude allowed

adversarial_steps: usize

Number of adversarial iterations

adversarial_lr: f64

Learning rate for adversarial updates

defense_strategy: String

Defense strategy: “spectral”, “robust_pca”, “consensus”, “adaptive”

consensus_threshold: f64

Consensus threshold for agreement-based defense

max_iter: usize

Maximum iterations for optimization

tolerance: f64

Convergence tolerance

random_state: Option<u64>

Random state for reproducibility

Implementations

impl AdversarialGraphLearning

pub fn new() -> Self

Create a new adversarial graph learning instance

pub fn k_neighbors(self, k: usize) -> Self

Set the number of neighbors for graph construction

pub fn robustness_lambda(self, lambda: f64) -> Self

Set the robustness parameter

pub fn max_perturbation(self, max_pert: f64) -> Self

Set the maximum perturbation magnitude

pub fn adversarial_steps(self, steps: usize) -> Self

Set the number of adversarial steps

pub fn adversarial_lr(self, lr: f64) -> Self

Set the adversarial learning rate

pub fn defense_strategy(self, strategy: String) -> Self

Set the defense strategy

pub fn consensus_threshold(self, threshold: f64) -> Self

Set the consensus threshold

pub fn max_iter(self, max_iter: usize) -> Self

Set the maximum iterations

pub fn tolerance(self, tol: f64) -> Self

Set the tolerance

pub fn random_state(self, seed: u64) -> Self

Set the random state

pub fn fit_robust( &self, features: ArrayView2<'_, f64>, labels: Option<ArrayView1<'_, i32>>, ) -> Result<Array2<f64>, SklearsError>

Learn a robust graph in the presence of adversarial perturbations

pub fn apply_attack( &self, features: ArrayView2<'_, f64>, attack: &AdversarialAttack, ) -> Result<Array2<f64>, SklearsError>

Apply adversarial attack to test robustness

pub fn evaluate_robustness( &self, original_graph: &Array2<f64>, attacked_graph: &Array2<f64>, ) -> Result<f64, SklearsError>

Evaluate robustness against attack

Trait Implementations

impl Clone for AdversarialGraphLearning

fn clone(&self) -> AdversarialGraphLearning

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Default for AdversarialGraphLearning

fn default() -> Self

Returns the “default value” for a type.