Module: aprender::graph

Public module of the aprender-core crate.

Source

crates/aprender-core/src/graph.rs or directory.

Example

use aprender::graph::{Graph, GraphCentrality};
// See `cargo doc -p aprender-core --open` for full API reference.

Module summary

aprender::graph is the classical graph-algorithms layer, built on a cache-friendly Compressed Sparse Row representation. It exposes a Graph type for directed and undirected graphs (weighted or unweighted), shortest paths (Dijkstra-based shortest_path), centrality measures (degree, PageRank, betweenness, closeness, eigenvector, Katz, harmonic) via the GraphCentrality extension trait, plus community detection (Louvain) and graph metrics (density, diameter, clustering_coefficient, assortativity).

Key types

TypeDescription
GraphCSR-backed graph. Constructors: new(directed), from_edges(...), from_weighted_edges(...).
EdgeSource / target / optional weight tuple used at construction time.
NodeIdType alias usize for contiguous node ids.
GraphCentralityExtension trait providing degree_centrality, pagerank, betweenness_centrality, etc.

Top-level Graph methods include num_nodes, num_edges, neighbors, shortest_path, louvain, modularity, density, diameter, clustering_coefficient, assortativity.

Usage patterns

Pattern 1: Build a triangle and measure centrality

use aprender::graph::{Graph, GraphCentrality};

// Undirected triangle 0 — 1 — 2 — 0
let g = Graph::from_edges(&[(0, 1), (1, 2), (2, 0)], false);
assert_eq!(g.num_nodes(), 3);

let dc = g.degree_centrality();
assert_eq!(dc.len(), 3);

let pr = g.pagerank(0.85, 100, 1e-6).expect("pagerank converges");
println!("pagerank scores: {:?}", pr);

Pattern 2: Shortest path and Louvain communities

use aprender::graph::Graph;

let g = Graph::from_weighted_edges(&[
    (0, 1, 1.0),
    (1, 2, 2.0),
    (2, 3, 1.0),
    (0, 3, 5.0),
], false);

let path = g.shortest_path(0, 3);
assert!(path.is_some(), "0 and 3 are connected");

let communities = g.louvain();
println!("found {} communities", communities.len());

See also

  • gnn — graph neural network layers (GCN, GAT, GIN, SAGE) that consume graph topology
  • primitives — Matrix / Vector for adjacency-matrix views
  • mining — frequent-pattern mining on graph-like transactional data

Full API

Run cargo doc -p aprender-core --open for the rendered rustdoc, or browse docs.rs/aprender for the published version.