Ruvector Gnn Wasm

@ruvector/gnn-wasm

WebAssembly-compiled Graph Neural Network layers providing browser-native and edge-compatible inference for GraphConv, GATLayer, SAGEConv, and GINConv with near-native performance and zero server dependency.

Quick Reference

Installation

Install: npx @ruvector/gnn-wasm@latest See Installation Guide for the full ecosystem.

Key API

WasmGNN

Main WASM-accelerated GNN inference engine.

Node.js:

import { WasmGNN } from '@ruvector/gnn-wasm';

const gnn = new WasmGNN({
  layers: ['gcn', 'gat', 'sage'],
  hiddenDim: 64,
  outputDim: 32,
});

Browser:

import init, { WasmGNN } from '@ruvector/gnn-wasm';

await init(); // Initialize WASM module

const gnn = new WasmGNN({
  layers: ['gcn', 'gat', 'sage'],
  hiddenDim: 64,
  outputDim: 32,
});

Constructor Options:

Methods:

WasmGraphConv

Single WASM-compiled graph convolution layer.

import init, { WasmGraphConv } from '@ruvector/gnn-wasm';
await init();

const conv = new WasmGraphConv({ inDim: 128, outDim: 64 });
const output = conv.forward(nodeFeatures, edgeIndex);

Constructor Options:

WasmGATLayer

WASM-compiled Graph Attention layer.

import init, { WasmGATLayer } from '@ruvector/gnn-wasm';
await init();

const gat = new WasmGATLayer({ inDim: 128, outDim: 64, heads: 8 });
const output = gat.forward(nodeFeatures, edgeIndex);

Constructor Options:

Common Patterns

Browser Graph Visualization

import init, { WasmGNN } from '@ruvector/gnn-wasm';

await init();

const gnn = new WasmGNN({ layers: ['gcn', 'gcn'], outputDim: 2 });
gnn.loadWeights(await fetch('/model/weights.bin').then(r => r.arrayBuffer()));

const embeddings = gnn.nodeEmbed({ features, edgeIndex });
// Use 2D embeddings for visualization layout
embeddings.forEach((emb, i) => {
  nodes[i].x = emb[0] * scale;
  nodes[i].y = emb[1] * scale;
});

Edge Device Inference

import { WasmGNN } from '@ruvector/gnn-wasm';

const gnn = new WasmGNN({
  layers: ['sage'],
  hiddenDim: 32,
  outputDim: 16,
  simd: true,
});

gnn.loadWeights(modelBuffer);
const prediction = gnn.forward(sensorFeatures, sensorGraph);
console.log(`Memory: ${gnn.getMemoryUsage().heapUsed} bytes`);

Bundler Configuration (Vite)

// vite.config.ts
export default {
  optimizeDeps: {
    exclude: ['@ruvector/gnn-wasm'],
  },
  plugins: [wasmPlugin()],
};

RAN DDD Context

Bounded Context: RANO Optimization

References

• API reference: See references/commands.md
• Full README
• npm