---
title: "Label Spreading in JavaScript with @kanaries/ml"
description: "Learn what Label Spreading does, when to use it, and how to run LabelSpreading in JavaScript or TypeScript with @kanaries/ml for browser and Node.js applications."
canonical_url: "https://ml.kanaries.net/docs/apis/semi_supervised/labelSpreading"
markdown_url: "https://ml.kanaries.net/docs/apis/semi_supervised/labelSpreading.md"
---
# Label Spreading in JavaScript

## Algorithm overview

LabelSpreading performs smoother semi-supervised label diffusion with regularization to reduce over-confident propagation.

This algorithm is especially useful when:

- You want semi-supervised learning with stronger stability than pure propagation.
- Graph neighborhoods are useful but somewhat noisy.
- You need better robustness for low-label datasets.

## JavaScript implementation

@kanaries/ml provides Label Spreading in JavaScript for semi-supervised problems where you want a smoother, regularized alternative to raw label propagation. That is useful for annotation tools and partially labeled datasets where graph-based learning should be robust to noisy neighborhoods.

Because the implementation runs in JS, teams can connect interactive labeling workflows, similarity features, and propagation-based learning without a separate Python service layer.

## Quick start

### LabelSpreading: Python and JavaScript / TypeScript

The Python example uses scikit-learn; the TypeScript example uses @kanaries/ml in browser or Node.js runtimes.

#### Python (scikit-learn)

```python
from sklearn.semi_supervised import LabelSpreading

X = [[0, 0], [0.1, 0.2], [1, 1], [1.1, 0.9]]
y = [0, -1, 1, -1]

model = LabelSpreading(alpha=0.2)
model.fit(X, y)
pred = model.predict(X)
```

#### JavaScript / TypeScript (@kanaries/ml)

```ts
import { SemiSupervised } from '@kanaries/ml';

const X = [[0, 0], [0.1, 0.2], [1, 1], [1.1, 0.9]];
const y = [0, -1, 1, -1];

const model = new SemiSupervised.LabelSpreading({ alpha: 0.2 });
model.fit(X, y);
const pred = model.predict(X);
```

### Quick JavaScript example

```ts
import { SemiSupervised } from '@kanaries/ml';

const X = [[0, 0], [0.1, 0.2], [1, 1], [1.1, 0.9]];
const y = [0, -1, 1, -1];

const model = new SemiSupervised.LabelSpreading({ alpha: 0.2 });
model.fit(X, y);
const pred = model.predict(X);
console.log(pred);
```

## Detailed API reference

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

Label spreading assigns labels to unlabeled data using a normalized graph
and soft clamping controlled by `alpha`.

### Methods

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

### Implementation workflow

1. Create labeled/unlabeled split and construct feature graph inputs.
2. Fit LabelSpreading and monitor convergence behavior.
3. Tune kernel and regularization parameters with validation labels.

### JavaScript deployment notes

- Prefer Label Spreading when graph noise or over-confident propagation is a concern.
- Tune kernel and regularization settings against a validation subset of known labels.
- Use it to improve label coverage before training a final supervised model rather than as the only production model.