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

## Algorithm overview

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

This algorithm is especially useful when:

- 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.

## JavaScript implementation

@kanaries/ml exposes Label Propagation in JavaScript for semi-supervised workflows where only a small portion of the dataset is labeled. This is useful for browser-based data tools, internal review apps, or Node.js services that want to spread labels through a similarity graph before downstream supervised training.

Keeping the algorithm in JS makes it easier to combine labeling interfaces, graph construction, and model-assisted annotation inside one product experience.

## Quick start

### LabelPropagation: 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 LabelPropagation

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

model = LabelPropagation()
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.LabelPropagation();
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.LabelPropagation();
model.fit(X, y);
const pred = model.predict(X);
console.log(pred);
```

## Detailed API reference

```ts
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[][]`

### 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

- Use Label Propagation when the similarity graph is meaningful and labels can reasonably spread through neighborhoods.
- Validate propagated labels on a trusted subset before using them downstream.
- It works best as a label-bootstrapping step, not as a replacement for careful annotation strategy.