Struct LinearThreshold 

pub struct LinearThreshold {
    pub adjacency: AdjList,
    pub num_nodes: usize,
    pub thresholds: Option<Vec<f64>>,
}

Linear Threshold (LT) diffusion model configuration.

Each node v has a threshold θ_v ∈ [0, 1] drawn at the start of each simulation. Node v becomes active when Σ_{u active} w(u, v) ≥ θ_v, where w(u, v) is the normalised in-edge weight.

Fields

adjacency: AdjList

Directed adjacency list: node → [(neighbour, weight)].

Weights represent influence of node on neighbour. They should be normalised so that Σ_{u} w(u, v) ≤ 1 for every v.

num_nodes: usize

Total number of nodes.

thresholds: Option<Vec<f64>>

Optional fixed thresholds per node (if None, drawn uniformly per run).

Implementations

impl LinearThreshold

pub fn new(adjacency: AdjList, num_nodes: usize) -> Self

Create a new LT model with random per-run thresholds.

pub fn with_thresholds(adjacency: AdjList, thresholds: Vec<f64>) -> Result<Self>

Create an LT model with fixed thresholds.

pub fn from_edges(edges: &[(usize, usize, f64)], num_nodes: usize) -> Self

Build from (source, target, weight) triples.

pub fn simulate(&self, seeds: &[usize]) -> Result<SimulationResult>

Run a single LT simulation.

pub fn expected_spread( &self, seeds: &[usize], num_simulations: usize, ) -> Result<f64>

Estimate expected spread.

Trait Implementations

impl Clone for LinearThreshold

fn clone(&self) -> LinearThreshold

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for LinearThreshold

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

Formats the value using the given formatter.