[−][src]Crate petgraph

`petgraph` is a graph data structure library.

Graphs are collections of nodes, and edges between nodes. `petgraph` provides several graph types (each differing in the tradeoffs taken in their internal representation), algorithms on those graphs, and functionality to output graphs in `graphviz` format. Both nodes and edges can have arbitrary associated data, and edges may be either directed or undirected.

Example

```use petgraph::graph::{NodeIndex, UnGraph};
use petgraph::algo::{dijkstra, min_spanning_tree};
use petgraph::data::FromElements;
use petgraph::dot::{Dot, Config};

// Create an undirected graph with `i32` nodes and edges with `()` associated data.
let g = UnGraph::<i32, ()>::from_edges(&[
(1, 2), (2, 3), (3, 4),
(1, 4)]);

// Find the shortest path from `1` to `4` using `1` as the cost for every edge.
let node_map = dijkstra(&g, 1.into(), Some(4.into()), |_| 1);
assert_eq!(&1i32, node_map.get(&NodeIndex::new(4)).unwrap());

// Get the minimum spanning tree of the graph as a new graph, and check that
// one edge was trimmed.
let mst = UnGraph::<_, _>::from_elements(min_spanning_tree(&g));
assert_eq!(g.raw_edges().len() - 1, mst.raw_edges().len());

// Output the tree to `graphviz` `DOT` format
println!("{:?}", Dot::with_config(&mst, &[Config::EdgeNoLabel]));
// graph {
//     0 [label="\"0\""]
//     1 [label="\"0\""]
//     2 [label="\"0\""]
//     3 [label="\"0\""]
//     1 -- 2
//     3 -- 4
//     2 -- 3
// }```

Graph types

Generic parameters

Each graph type is generic over a handful of parameters. All graphs share 3 common parameters, `N`, `E`, and `Ty`. This is a broad overview of what those are. Each type's documentation will have finer detail on these parameters.

`N` & `E` are called weights in this implementation, and are associated with nodes and edges respectively. They can generally be of arbitrary type, and don't have to be what you might conventionally consider weight-like. For example, using `&str` for `N` will work. Many algorithms that require costs let you provide a cost function that translates your `N` and `E` weights into costs appropriate to the algorithm. Some graph types and choices do impose bounds on `N` or `E`. `min_spanning_tree` for example requires edge weights that implement `PartialOrd`. `GraphMap` requires node weights that can serve as hash map keys, since that graph type does not create standalone node indices.

`Ty` controls whether edges are `Directed` or `Undirected`.

`Ix` appears on graph types that use indices. It is exposed so you can control the size of node and edge indices, and therefore the memory footprint of your graphs. Allowed values are `u8`, `u16`, `u32`, and `usize`, with `u32` being the default.

Shorthand types

Each graph type vends a few shorthand type definitions that name some specific generic choices. For example, `DiGraph<_, _>` is shorthand for `Graph<_, _, Directed>`. `UnMatrix<_, _>` is shorthand for `MatrixGraph<_, _, Undirected>`. Each graph type's module documentation lists the available shorthand types.

Crate features

Re-exports

 `pub use crate::graph::Graph;` `pub use crate::Direction::Incoming;` `pub use crate::Direction::Outgoing;`

Modules

 algo Graph algorithms. csr Compressed Sparse Row (CSR) is a sparse adjacency matrix graph. data Graph traits for associated data and graph construction. dot Simple graphviz dot file format output. graph `Graph` is a graph datastructure using an adjacency list representation. graphmap `GraphMap` is a graph datastructure where node values are mapping keys. matrix_graph `MatrixGraph` is a graph datastructure backed by an adjacency matrix. prelude Commonly used items. stable_graph `StableGraph` keeps indices stable across removals. unionfind `UnionFind` is a disjoint-set data structure. visit Graph traits and graph traversals.

Enums

 Directed Marker type for a directed graph. Direction Edge direction. Undirected Marker type for an undirected graph.

Traits

 EdgeType A graph's edge type determines whether it has directed edges or not. IntoWeightedEdge Convert an element like `(i, j)` or `(i, j, w)` into a triple of source, target, edge weight.