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/*!
# Graphina Crate
High-level graph data science library. Provides graph types, algorithms, and analytics.
## Module Overview
* `core` – Always enabled: basic graph types, builders, IO, serialization, paths, validation.
* `centrality` *(feature: centrality)* – Node/edge importance measures (Result-based APIs).
* `community` *(feature: community)* – Community detection and clustering (Result-based APIs).
* `links` *(feature: links)* – Link prediction algorithms.
* `metrics` *(feature: metrics)* – Graph and node metrics (diameter, radius, clustering, etc.).
* `mst` *(feature: mst)* – Minimum spanning tree algorithms.
* `traversal` *(feature: traversal)* – BFS/DFS and related traversal strategies.
* `approximation` *(feature: approximation)* – Heuristics for NP-hard problems.
* `parallel` *(feature: parallel)* – Parallel implementations for selected algorithms.
* `subgraphs` *(feature: subgraphs)* – Induced subgraph and ego network utilities.
* `visualization` *(feature: visualization)* – Layouts and render helpers (ASCII/HTML/SVG/PNG).
* `core::pool` *(feature: pool)* – Experimental memory pooling utilities.
## API Conventions
Algorithms return `Result<_, graphina::core::error::GraphinaError>` for robust error handling.
Selector-style helpers that pick nodes (like `voterank`) may return plain collections.
Enable only required features to minimize size and compile time.
## Stability Notes
The `pool` feature is experimental; API subject to change. Gate usage with `cfg(feature = "pool")`.
*/
/// Approximation algorithms for NP-hard problems.
/// Community detection and clustering algorithms.
/// Core graph types and utilities.
/// Link prediction algorithms.