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