A library that provides a collection of high-performant graph algorithms. This crate builds on top of the graph_builder crate, which can be used as a building block for custom graph algorithms.
graph_builder provides implementations for directed and undirected graphs.
Graphs can be created programatically or read from custom input formats in a
type-safe way. The library uses rayon
to parallelize all steps during graph creation. The implementation uses a
Compressed-Sparse-Row (CSR) data structure which is tailored for fast and
concurrent access to the graph topology.
graph provides graph algorithms which take graphs created using graph_builder
as input. The algorithm implementations are designed to run efficiently on
large-scale graphs with billions of nodes and edges.
Note: The development is mainly driven by Neo4j developers. However, the library is not an official product of Neo4j.
A graph consists of nodes and edges where edges connect exactly two nodes. A graph can be either directed, i.e., an edge has a source and a target node or undirected where there is no such distinction.
In a directed graph, each node u has outgoing and incoming neighbors. An
outgoing neighbor of node u is any node v for which an edge (u, v)
exists. An incoming neighbor of node u is any node v for which an edge
(v, u) exists.
In an undirected graph there is no distinction between source and target
node. A neighbor of node u is any node v for which either an edge (u, v) or (v, u) exists.
The library provides a builder that can be used to construct a graph from a given list of edges.
For example, to create a directed graph that uses usize as node
identifier, one can use the builder like so:
use graph::prelude::*;
let graph: DirectedCsrGraph<usize> = GraphBuilder::new()
.csr_layout(CsrLayout::Sorted)
.edges(vec![(0, 1), (0, 2), (1, 2), (1, 3), (2, 3)])
.build();
assert_eq!(graph.node_count(), 4);
assert_eq!(graph.edge_count(), 5);
assert_eq!(graph.out_degree(1), 2);
assert_eq!(graph.in_degree(1), 1);
assert_eq!(graph.out_neighbors(1).as_slice(), &[2, 3]);
assert_eq!(graph.in_neighbors(1).as_slice(), &[0]);To build an undirected graph using u32 as node identifer, we only need to
change the expected types:
use graph::prelude::*;
let graph: UndirectedCsrGraph<u32> = GraphBuilder::new()
.csr_layout(CsrLayout::Sorted)
.edges(vec![(0, 1), (0, 2), (1, 2), (1, 3), (2, 3)])
.build();
assert_eq!(graph.node_count(), 4);
assert_eq!(graph.edge_count(), 5);
assert_eq!(graph.degree(1), 3);
assert_eq!(graph.neighbors(1).as_slice(), &[0, 2, 3]);Check out the graph_builder crate for for more examples on how to build graphs from various input formats.
In the following we will demonstrate running Page Rank, a graph algorithm to determine the importance of nodes in a graph based on the number and quality of their incoming edges.
Page Rank requires a directed graph and returns the rank value for each node.
use graph::prelude::*;
// https://en.wikipedia.org/wiki/PageRank#/media/File:PageRanks-Example.svg
let graph: DirectedCsrGraph<usize> = GraphBuilder::new()
.edges(vec![
(1,2), // B->C
(2,1), // C->B
(4,0), // D->A
(4,1), // D->B
(5,4), // E->D
(5,1), // E->B
(5,6), // E->F
(6,1), // F->B
(6,5), // F->E
(7,1), // G->B
(7,5), // F->E
(8,1), // G->B
(8,5), // G->E
(9,1), // H->B
(9,5), // H->E
(10,1), // I->B
(10,5), // I->E
(11,5), // J->B
(12,5), // K->B
])
.build();
let (ranks, iterations, _) = page_rank(&graph, PageRankConfig::new(10, 1E-4, 0.85));
assert_eq!(iterations, 10);
let expected = vec![
0.024064068,
0.3145448,
0.27890152,
0.01153846,
0.029471997,
0.06329483,
0.029471997,
0.01153846,
0.01153846,
0.01153846,
0.01153846,
0.01153846,
0.01153846,
];
assert_eq!(ranks, expected);License: MIT
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