SemiSupervised.LabelPropagation

interface LabelPropagationOptions {
    kernel?: 'rbf' | 'knn' | ((X: number[][], Y: number[][]) => number[][]);
    gamma?: number;
    nNeighbors?: number;
    maxIter?: number;
    tol?: number;
}
constructor(options: LabelPropagationOptions = {})

Label propagation assigns labels to unlabeled data by propagating information from labeled points across a graph defined by a kernel.

Methods

• fit(trainX: number[][], trainY: number[]): void
• predict(testX: number[][]): number[]
• predictProba(testX: number[][]): number[][]

Example

const lp = new LabelPropagation();
lp.fit(trainX, trainY);
const preds = lp.predict(testX);

Practical guide: LabelPropagation in JavaScript and TypeScript

LabelPropagation spreads labels through similarity graphs to leverage unlabeled data in transductive settings.

When to use LabelPropagation

• Only a small subset of samples is labeled.
• Similarity graph structure reflects class continuity.
• You need to bootstrap labels before training a final supervised model.

Implementation workflow

1. Build feature representations and seed reliable initial labels.
2. Fit LabelPropagation and inspect propagated label confidence.
3. Validate on known labels and tune graph-related hyperparameters.

JavaScript deployment notes

• Prefer feature scaling for distance-based and gradient-based algorithms to improve stability.
• In browser apps, run heavy training in Web Workers to keep UI interactions smooth.
• Keep a simple baseline from the same module as a fallback model for comparison.

Search intents this page targets

• LabelPropagation JavaScript
• LabelPropagation TypeScript
• LabelPropagation browser machine learning
• @kanaries/ml LabelPropagation