ruvector-graph-transformer-wasm

WebAssembly bindings for RuVector Graph Transformer — proof-gated graph attention, verified training, and 8 specialized graph layers running client-side in the browser.

Run the full graph transformer in any browser tab — no server, no API calls, no data leaving the device. Every graph mutation is formally verified client-side, so your users get the same mathematical safety guarantees as the Rust version. The WASM binary is size-optimized and loads in milliseconds.

Install

# With wasm-pack (recommended)
wasm-pack build crates/ruvector-graph-transformer-wasm --target web

# Or from npm (when published)
npm install ruvector-graph-transformer-wasm

Quick Start

import init, { JsGraphTransformer } from "ruvector-graph-transformer-wasm";

await init();
const gt = new JsGraphTransformer();
console.log(gt.version()); // "2.0.4"

// Proof-gated mutation
const gate = gt.create_proof_gate(128);
const proof = gt.prove_dimension(128, 128);
console.log(proof.verified); // true

// 82-byte attestation for RVF witness chains
const attestation = gt.create_attestation(proof.proof_id);
console.log(attestation.length); // 82

// Sublinear attention — O(n log n)
const result = gt.sublinear_attention(
  new Float32Array([0.1, 0.2, 0.3, 0.4]),
  [{ src: 0, tgt: 1 }, { src: 0, tgt: 2 }],
  4, 2
);

// Verified training step with certificate
const step = gt.verified_training_step(
  [1.0, 2.0], [0.1, 0.2], 0.01
);
console.log(step.weights, step.certificate);

// Physics: symplectic integration
const state = gt.hamiltonian_step([1.0, 0.0], [0.0, 1.0], 0.01);
console.log(state.energy);

// Biological: spiking attention
const spikes = gt.spiking_attention(
  [0.5, 1.5, 0.3], [[1], [0, 2], [1]], 1.0
);

// Manifold: mixed-curvature distance
const d = gt.product_manifold_distance(
  [1, 0, 0, 1], [0, 1, 1, 0], [0.0, -1.0]
);

// Temporal: causal masking
const scores = gt.causal_attention(
  [1.0, 0.0],
  [[1.0, 0.0], [0.0, 1.0]],
  [1.0, 2.0]
);

// Economic: Nash equilibrium
const nash = gt.game_theoretic_attention(
  [1.0, 0.5, 0.8],
  [{ src: 0, tgt: 1 }, { src: 1, tgt: 2 }]
);
console.log(nash.converged);

// Stats
console.log(gt.stats());

API

Proof-Gated Operations

Method	Returns	Description
`new JsGraphTransformer(config?)`	`JsGraphTransformer`	Create transformer instance
`version()`	`string`	Crate version
`create_proof_gate(dim)`	`object`	Create proof gate for dimension
`prove_dimension(expected, actual)`	`object`	Prove dimension equality
`create_attestation(proof_id)`	`Uint8Array`	82-byte proof attestation
`verify_attestation(bytes)`	`boolean`	Verify attestation from bytes
`compose_proofs(stages)`	`object`	Type-checked pipeline composition

Sublinear Attention

Method	Returns	Description
`sublinear_attention(q, edges, dim, k)`	`object`	Graph-sparse top-k attention
`ppr_scores(source, adj, alpha)`	`Float64Array`	Personalized PageRank scores

Physics-Informed

Method	Returns	Description
`hamiltonian_step(positions, momenta, dt)`	`object`	Symplectic leapfrog step
`verify_energy_conservation(before, after, tol)`	`object`	Energy conservation proof

Biological

Method	Returns	Description
`spiking_attention(spikes, edges, threshold)`	`Float64Array`	Event-driven spiking attention
`hebbian_update(pre, post, weights, lr)`	`Float64Array`	Hebbian weight update
`spiking_step(features, adjacency)`	`object`	Full spiking step over feature matrix

Verified Training

Method	Returns	Description
`verified_step(weights, gradients, lr)`	`object`	SGD step + proof receipt
`verified_training_step(features, targets, weights)`	`object`	Training step + certificate

Manifold

Method	Returns	Description
`product_manifold_distance(a, b, curvatures)`	`number`	Mixed-curvature distance
`product_manifold_attention(features, edges)`	`object`	Product manifold attention

Temporal-Causal

Method	Returns	Description
`causal_attention(query, keys, timestamps)`	`Float64Array`	Temporally masked attention
`causal_attention_graph(features, timestamps, edges)`	`Float64Array`	Causal graph attention
`granger_extract(history, num_nodes, num_steps)`	`object`	Granger causality DAG

Economic

Method	Returns	Description
`game_theoretic_attention(features, edges)`	`object`	Nash equilibrium attention

Method	Returns	Description
`stats()`	`object`	Aggregate proof/attestation statistics
`reset()`	`void`	Reset all internal state

Building

# Web target (recommended for browsers)
wasm-pack build crates/ruvector-graph-transformer-wasm --target web

# Node.js target
wasm-pack build crates/ruvector-graph-transformer-wasm --target nodejs

# Cargo check
cargo check -p ruvector-graph-transformer-wasm

Bundle Size

The WASM binary is optimized for size with opt-level = "s", LTO, and single codegen unit.

Related Packages

Package	Description
`ruvector-graph-transformer`	Core Rust crate (186 tests)
`@ruvector/graph-transformer`	Node.js NAPI-RS bindings
`ruvector-verified-wasm`	Formal verification WASM bindings

License

MIT

ruvector-graph-transformer-wasm 2.0.4