ruvector-graph-transformer

ruvector-graph-transformer

A graph neural network where every operation is mathematically proven correct before it runs.

[dependencies]
ruvector-graph-transformer = "2.0"

Most graph neural networks let you modify data freely -- add nodes, change weights, update edges -- with no safety guarantees. If a bug corrupts your graph, you find out later (or never). This crate takes a different approach: every mutation to graph state requires a formal proof that the operation is valid. No proof, no access. Think of it like a lock on every piece of data that can only be opened with the right mathematical key.

On top of that safety layer, 8 specialized modules bring cutting-edge graph intelligence: attention that scales to millions of nodes without checking every pair, physics simulations that conserve energy by construction, neurons that only fire when they should, training that automatically rolls back bad gradient steps, and geometry that works in curved spaces instead of assuming everything is flat.

The result is a graph transformer you can trust: if it produces an answer, that answer was computed correctly. Part of the RuVector ecosystem.

	Standard GNN	ruvector-graph-transformer
Mutation safety	Unchecked -- bugs corrupt silently	Proof-gated: no mutation without formal witness
Attention complexity	O(n^2) -- slows down on large graphs	O(n log n) sublinear via LSH/PPR/spectral
Training guarantees	Hope for the best	Verified: certificates, delta-apply rollback, fail-closed
Geometry	Euclidean only -- assumes flat space	Product manifolds S^n x H^m x R^k
Causality	No enforcement	Temporal masking + Granger causality extraction
Incentive alignment	Not considered	Nash equilibrium + Shapley attribution
Platforms	Python only	Rust + WASM + Node.js (NAPI-RS)

Key Features

Feature	What It Does	Why It Matters
Proof-Gated Mutation	Every write to graph state requires a formal proof	Bugs cannot silently corrupt your data -- invalid operations are rejected before they happen
Sublinear Attention	LSH-bucketed, PPR-sampled, and spectral sparsification	Scales to millions of nodes where O(n^2) attention would be unusable
Verified Training	Delta-apply rollback with BLAKE3-hashed certificates	Bad gradient steps are automatically rolled back; every training step is auditable
Physics-Informed	Hamiltonian dynamics, gauge equivariant message passing	Simulations conserve energy by construction -- no drift over long runs
Biological	Spiking attention, Hebbian/STDP learning, dendritic branching	Model brain-like dynamics where neurons only fire when they should
Self-Organizing	Morphogenetic fields, developmental programs, graph coarsening	Graphs that grow, adapt, and restructure themselves
Manifold Geometry	Product manifolds S^n x H^m x R^k, Riemannian Adam	Work in curved spaces where Euclidean assumptions break down
Temporal-Causal	Causal masking, continuous-time ODE, Granger causality	Enforce cause-before-effect and extract causal relationships from time series
Economic	Nash equilibrium attention, Shapley attribution	Align incentives in multi-agent graphs and attribute value fairly

Modules

8 feature-gated modules, each backed by an Architecture Decision Record:

Module	Feature Flag	ADR	What It Does
Proof-Gated Mutation	always on	ADR-047	ProofGate<T>, MutationLedger, ProofScope, EpochBoundary
Sublinear Attention	sublinear	ADR-048	LSH-bucket, PPR-sampled, spectral sparsification
Physics-Informed	physics	ADR-051	Hamiltonian dynamics, gauge equivariant MP, Lagrangian attention, conservative PDE
Biological	biological	ADR-052	Spiking attention, Hebbian/STDP learning, dendritic branching, inhibition strategies
Self-Organizing	self-organizing	--	Morphogenetic fields, developmental programs, graph coarsening
Verified Training	verified-training	ADR-049	Training certificates, delta-apply rollback, LossStabilityBound, EnergyGate
Manifold	manifold	ADR-055	Product manifolds, Riemannian Adam, geodesic MP, Lie group equivariance
Temporal-Causal	temporal	ADR-053	Causal masking, retrocausal attention, continuous-time ODE, Granger causality
Economic	economic	ADR-054	Nash equilibrium attention, Shapley attribution, incentive-aligned MPNN

Quick Start

[dependencies]
ruvector-graph-transformer = "2.0"

# Or with all modules:
ruvector-graph-transformer = { version = "2.0", features = ["full"] }

Proof-Gated Mutation

Every mutation to graph state passes through a proof gate:

use ruvector_graph_transformer::{ProofGate, GraphTransformer, GraphTransformerConfig};
use ruvector_verified::ProofEnvironment;

// Create a proof environment and graph transformer
let mut env = ProofEnvironment::new();
let gt = GraphTransformer::with_defaults();

// Gate a value behind a proof
let gate: ProofGate<Vec<f32>> = gt.create_gate(vec![1.0; 128]);

// Mutation requires proof -- no proof, no access
let proof_id = ruvector_verified::prove_dim_eq(&mut env, 128, 128).unwrap();
let mutated = gate.mutate_with_proof(&env, proof_id, |v| {
    v.iter_mut().for_each(|x| *x *= 2.0);
}).unwrap();

Sublinear Attention

use ruvector_graph_transformer::sublinear_attention::SublinearGraphAttention;
use ruvector_graph_transformer::config::SublinearConfig;

let config = SublinearConfig {
    lsh_buckets: 16,
    ppr_samples: 8,
    sparsification_factor: 0.5,
};
let attn = SublinearGraphAttention::new(128, config);

// O(n log n) instead of O(n^2)
let features = vec![vec![0.5f32; 128]; 1000];
let outputs = attn.lsh_attention(&features).unwrap();

Verified Training

use ruvector_graph_transformer::verified_training::{VerifiedTrainer, TrainingInvariant};
use ruvector_graph_transformer::config::VerifiedTrainingConfig;

let config = VerifiedTrainingConfig {
    fail_closed: true,  // reject step if any invariant fails
    ..Default::default()
};
let mut trainer = VerifiedTrainer::new(
    config,
    vec![
        TrainingInvariant::LossStabilityBound { window: 10, max_deviation: 0.1 },
        TrainingInvariant::WeightNormBound { max_norm: 100.0 },
    ],
);

// Delta-apply: gradients go to scratch buffer, commit only if invariants pass
let result = trainer.step(&weights, &gradients, lr).unwrap();
assert!(result.certificate.is_some()); // BLAKE3-hashed training certificate

Physics-Informed Layers

use ruvector_graph_transformer::physics::HamiltonianGraphNet;
use ruvector_graph_transformer::config::PhysicsConfig;

let config = PhysicsConfig::default();
let mut hgn = HamiltonianGraphNet::new(config);

// Symplectic leapfrog preserves energy
let (new_q, new_p) = hgn.step(&positions, &momenta, &edges, dt);
assert!(hgn.energy_conserved(1e-6)); // formal conservation proof

Manifold Operations

use ruvector_graph_transformer::manifold::{ProductManifoldAttention, ManifoldType};
use ruvector_graph_transformer::config::ManifoldConfig;

let config = ManifoldConfig {
    spherical_dim: 64,
    hyperbolic_dim: 32,
    euclidean_dim: 32,
    curvature: -1.0,
};
let attn = ProductManifoldAttention::new(config);

// Attention in S^64 x H^32 x R^32
let outputs = attn.forward(&features, &edges).unwrap();

Feature Flags

[features]
default = ["sublinear", "verified-training"]
full = ["sublinear", "physics", "biological", "self-organizing",
        "verified-training", "manifold", "temporal", "economic"]

Flag	Default	Adds
sublinear	yes	LSH, PPR, spectral attention
verified-training	yes	Training certificates, delta-apply rollback
physics	no	Hamiltonian, gauge, Lagrangian, PDE layers
biological	no	Spiking, Hebbian, STDP, dendritic layers
self-organizing	no	Morphogenetic fields, developmental programs
manifold	no	Product manifolds, Riemannian Adam, Lie groups
temporal	no	Causal masking, Granger causality, ODE
economic	no	Nash equilibrium, Shapley, incentive-aligned MPNN

Architecture

ruvector-graph-transformer
├── proof_gated.rs          <- ProofGate<T>, MutationLedger, attestation chains
├── sublinear_attention.rs  <- O(n log n) attention via LSH/PPR/spectral
├── physics.rs              <- Energy-conserving Hamiltonian/Lagrangian dynamics
├── biological.rs           <- Spiking networks, Hebbian plasticity, STDP
├── self_organizing.rs      <- Morphogenetic fields, reaction-diffusion growth
├── verified_training.rs    <- Certified training with delta-apply rollback
├── manifold.rs             <- Product manifold S^n x H^m x R^k geometry
├── temporal.rs             <- Causal masking, Granger causality, ODE integration
├── economic.rs             <- Nash equilibrium, Shapley values, mechanism design
├── config.rs               <- Per-module configuration with sensible defaults
├── error.rs                <- Unified error composing 4 sub-crate errors
└── lib.rs                  <- Unified entry point with feature-gated re-exports

Dependencies

ruvector-graph-transformer
├── ruvector-verified    <- formal proofs, attestations, gated routing
├── ruvector-gnn         <- base GNN message passing
├── ruvector-attention   <- scaled dot-product attention
├── ruvector-mincut      <- graph structure operations
├── ruvector-solver      <- sparse linear systems
└── ruvector-coherence   <- coherence measurement

Bindings

Platform	Package	Install
WASM	ruvector-graph-transformer-wasm	wasm-pack build
Node.js	ruvector-graph-transformer-node	npm install @ruvector/graph-transformer

Tests

# Default features (sublinear + verified-training)
cargo test -p ruvector-graph-transformer

# All modules
cargo test -p ruvector-graph-transformer --features full

# Individual module
cargo test -p ruvector-graph-transformer --features physics

163 unit tests + 23 integration tests = 186 total, all passing.

ADR Documentation

ADR	Title
ADR-046	Unified Graph Transformer Architecture
ADR-047	Proof-Gated Mutation Protocol
ADR-048	Sublinear Graph Attention
ADR-049	Verified Training Pipeline
ADR-050	WASM + Node.js Bindings
ADR-051	Physics-Informed Graph Layers
ADR-052	Biological Graph Layers
ADR-053	Temporal-Causal Graph Layers
ADR-054	Economic Graph Layers
ADR-055	Manifold Graph Layers

License

MIT License - see LICENSE for details.

---

Part of RuVector - Built by rUv

Documentation | Crates.io | GitHub