sproink

In-memory spreading activation engine with CSR graph and C FFI.

Sproink models associative memory as energy spreading through a weighted graph. Seed nodes are activated, energy propagates along edges with decay, and the result is a ranked set of associated nodes -- useful for recommendation, semantic search, and knowledge retrieval.

Features

• CSR graph -- compressed sparse row storage for cache-friendly traversal
• 3-step propagation -- spread, squash (sigmoid), inhibit per iteration
• Lateral inhibition -- top-M winner-take-all competition suppresses noise
• Oja's rule -- Hebbian weight updates with normalization for online learning
• Jaccard affinity -- tag-based edge generation from shared attributes
• Rayon parallelism -- parallel activation spreading across nodes
• C FFI -- full C API with opaque handles (sproink.h), usable from any language

Quick Start

Rust

use sproink::*;

let graph = CsrGraph::build(3, vec![
    EdgeInput { source: NodeId(0), target: NodeId(1), weight: EdgeWeight::new(0.8).unwrap(), kind: EdgeKind::Positive },
    EdgeInput { source: NodeId(1), target: NodeId(2), weight: EdgeWeight::new(0.6).unwrap(), kind: EdgeKind::Positive },
]);

let engine = Engine::new(graph);
let config = PropagationConfig::builder().build(); // defaults: 3 steps, decay=0.7, spread=0.85
let seeds = vec![Seed { node: NodeId(0), activation: Activation::new(1.0).unwrap() }];

let results = engine.activate(&seeds, &config);
for r in &results {
    println!("node={:?} activation={:.3} distance={}", r.node, r.activation.get(), r.distance);
}

C FFI

#include "sproink.h"

uint32_t sources[] = {0, 1};
uint32_t targets[] = {1, 2};
double weights[] = {0.8, 0.6};
uint8_t kinds[] = {0, 0};

SproinkGraph *g = sproink_graph_build(3, 2, sources, targets, weights, kinds);
SproinkResults *r = sproink_activate(g, 1, (uint32_t[]){0}, (double[]){1.0},
    3, 0.85, 1.0, 0.001, 5.0, 0.5, false, 0.15, 5);

uint32_t len = sproink_results_len(r);
// ... read results ...

sproink_results_free(r);
sproink_graph_free(g);

Link against libsproink.so / libsproink.dylib / sproink.dll (cdylib) or libsproink.a (staticlib).

Architecture

Module	Purpose
graph	CSR graph storage, edge types, construction
engine	Propagation loop: spread, squash, inhibit
squash	Sigmoid activation squashing
inhibition	Top-M lateral inhibition
hebbian	Oja's rule for Hebbian weight learning
affinity	Jaccard similarity for edge generation
types	Core newtypes (NodeId, Activation, EdgeWeight, etc.)
error	Error types
ffi	C-compatible API surface (sproink.h)

Algorithm

1. Seed -- initial nodes receive activation energy
2. Spread -- energy flows along outgoing edges, scaled by weight and decay
3. Squash -- sigmoid function normalizes activations
4. Inhibit -- top-M competition suppresses low activations (optional)
5. Repeat steps 2-4 for max_steps iterations
6. Learn -- Oja's rule updates edge weights from co-activation pairs (optional)

Development

make ci        # fmt-check, lint, test, build
make test      # cargo test
make bench     # criterion benchmarks
make coverage  # generate lcov coverage

See CONTRIBUTING.md for full development workflow.

License

Apache 2.0 -- Copyright 2026 Nic van Dessel