Module conversion 

Conversion of graphs to matrices, edge lists, Prüfer sequences, and between directed and undirected graphs (igraph_conversion.h).

This module binds the whole of igraph’s conversion chapter. Every function is a method of Graph:

Method	C function	Result
Graph::get_adjacency	igraph_get_adjacency	dense adjacency Matrix (optionally weighted)
Graph::get_adjacency_sparse	igraph_get_adjacency_sparse	sparse adjacency matrix as a CooMatrix
Graph::get_stochastic	igraph_get_stochastic	row- or column-stochastic transition Matrix
Graph::get_stochastic_sparse	igraph_get_stochastic_sparse	the same, as a CooMatrix
Graph::get_edgelist	igraph_get_edgelist	flat edge list, row- or column-wise
Graph::to_directed / Graph::into_directed	igraph_to_directed	undirected → directed, in place / by value
Graph::to_undirected / Graph::into_undirected	igraph_to_undirected	directed → undirected, in place / by value
Graph::to_undirected_with_comb	igraph_to_undirected	the same, combining the edge attributes of merged edges
Graph::to_prufer	igraph_to_prufer	Prüfer sequence of a labelled tree

Sparse results

igraph returns sparse matrices as igraph_sparsemat_t (a CXSparse matrix, exposed by this crate as SparseMat). The sparse wrappers of this module read that matrix out into a plain-Rust CooMatrix (coordinate / triplet format), with duplicate entries summed up and the entries sorted in row-major order. It is easy to inspect, compare and iterate over; convert it back with CooMatrix::to_sparsemat when you need the sparse linear algebra of crate::linalg (solvers, factorizations, ARPACK eigensolvers).

See also

• The inverse conversions are constructors in crate::constructors: Graph::adjacency, Graph::weighted_adjacency, Graph::sparse_adjacency and Graph::sparse_weighted_adjacency build a graph from an adjacency matrix, Graph::from_flat_edges from the output of Graph::get_edgelist, and Graph::from_prufer from a Prüfer sequence.
• Other matrices of a graph: the Laplacian (Graph::get_laplacian, Graph::get_laplacian_sparse) in crate::structural, and the spectra of adjacency matrices (Graph::eigen_adjacency) in crate::linalg.
• Random walks: Graph::random_walk simulates the walk whose transition matrix is Graph::get_stochastic, and Graph::pagerank computes its (damped) stationary distribution.
• Directedness: Graph::is_mutual, Graph::has_mutual and Graph::reciprocity tell how much ToUndirected::Mutual and ToUndirected::Collapse will differ; Graph::is_dag checks the result of ToDirected::Acyclic.

Example

use igraph::prelude::*;

// A directed 3-cycle 0 → 1 → 2 → 0.
let mut g = Graph::from_edges(&[(0, 1), (1, 2), (2, 0)], 3, true).unwrap();
let a = g.get_adjacency(GetAdjacency::Both, None, Loops::Twice).unwrap();
assert_eq!(a.to_rows(), vec![vec![0.0, 1.0, 0.0], vec![0.0, 0.0, 1.0], vec![1.0, 0.0, 0.0]]);

// Forget the directions: the adjacency matrix becomes symmetric.
g.to_undirected(ToUndirected::Collapse).unwrap();
let a = g.get_adjacency(GetAdjacency::Both, None, Loops::Twice).unwrap();
assert_eq!(a, a.transposed());
// ... and it is enough to rebuild the graph.
let h = Graph::adjacency(&a, Adjacency::Undirected, Loops::Twice).unwrap();
assert_eq!(h.ecount(), 3);

// Random walk transition probabilities: each row sums to one.
let p = g.get_stochastic(false, None).unwrap();
assert!(p.rows().all(|r| (r.iter().sum::<f64>() - 1.0).abs() < 1e-12));

// A path is a tree: its Prüfer sequence lists the inner vertices.
let path = Graph::from_edges(&[(0, 1), (1, 2), (2, 3)], 4, false).unwrap();
assert_eq!(path.to_prufer().unwrap(), vec![1, 2]);
assert!(Graph::from_prufer(&[1, 2]).unwrap().is_same_graph(&path).unwrap());

On a larger scale, with Zachary’s karate club network: the sparse adjacency matrix stores 2|E| entries, and its row sums are the degrees.

use igraph::prelude::*;

let karate = Graph::famous("Zachary").unwrap();
let a = karate.get_adjacency_sparse(GetAdjacency::Both, None, Loops::Twice).unwrap();
assert_eq!(a.nnz(), 2 * karate.ecount());
let degrees = karate.degree(.., NeighborMode::All, Loops::Twice).unwrap();
let row_sums: Vec<i64> = a.row_sums().iter().map(|&s| s as i64).collect();
assert_eq!(row_sums, degrees);

Structs

CooMatrix

A sparse real matrix in coordinate (triplet) format.