Incremental graph sparsifier that compresses large graphs into a 'shadow graph' preserving key properties like connectivity, cuts, and flow. Uses random walks for importance scoring, spectral sampling, union-find backbone, and periodic auditing to maintain accuracy without full rebuilds.
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Maintains a small graph behaving like the large one for efficient queries.
| Component | What it does |
|---|---|
| Backbone | Spanning forest guaranteeing connectivity |
| Importance scorer | Random walks for edge importance |
| Spectral sampler | Proportional edge keeping, reweighting |
| Auditor | Verifies approximation quality |
49 tests, benchmarks, WASM bindings.
cargo add ruvector-sparsifier
Free and open-source.