From 8cd5a4a2089570ce531340507adad24809b58999 Mon Sep 17 00:00:00 2001 From: Matthew Treinish Date: Tue, 17 Oct 2023 09:45:35 -0400 Subject: [PATCH 1/3] Add barabasi_albert_graph random graph functions MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit This commit adds new random graph functions to rustworkx and rustworkx-core to implement a random graph generator using the Barabási–Albert preferential attachment method. It takes an input graph (defaulting to a star graph) and then extends it to a given size. --- .../api/random_graph_generator_functions.rst | 2 + .../add-albert-graph-5a7d393e1fe18e9d.yaml | 20 ++ rustworkx-core/src/generators/mod.rs | 1 + rustworkx-core/src/generators/random_graph.rs | 179 +++++++++++++++++- src/lib.rs | 2 + src/random_graph.rs | 122 ++++++++++++ tests/rustworkx_tests/test_random.py | 42 ++++ 7 files changed, 366 insertions(+), 2 deletions(-) create mode 100644 releasenotes/notes/add-albert-graph-5a7d393e1fe18e9d.yaml diff --git a/docs/source/api/random_graph_generator_functions.rst b/docs/source/api/random_graph_generator_functions.rst index 59ffbe63b..624659a3f 100644 --- a/docs/source/api/random_graph_generator_functions.rst +++ b/docs/source/api/random_graph_generator_functions.rst @@ -11,3 +11,5 @@ Random Graph Generator Functions rustworkx.directed_gnm_random_graph rustworkx.undirected_gnm_random_graph rustworkx.random_geometric_graph + rustworkx.barabasi_albert_graph + rustworkx.directed_barabasi_albert_graph diff --git a/releasenotes/notes/add-albert-graph-5a7d393e1fe18e9d.yaml b/releasenotes/notes/add-albert-graph-5a7d393e1fe18e9d.yaml new file mode 100644 index 000000000..5cb3f8e48 --- /dev/null +++ b/releasenotes/notes/add-albert-graph-5a7d393e1fe18e9d.yaml @@ -0,0 +1,20 @@ +--- +features: + - | + Added two new random graph generator functions, + :func:`.directed_barabasi_albert_graph` and :func:`.barabasi_albert_graph`, + to generate a random graph using Barabási–Albert preferential attachment to + extend an input graph. For example: + + .. jupyter-execute:: + + import rustworkx + from rustworkx.visualization import graphviz_draw + + starting_graph = rustworkx.generators.path_graph(10) + random_graph = rustworkx.barabasi_albert_graph(20, 10, initial_graph=starting_graph) + graphviz_draw(random_graph, method="neato") + - | + Added a new function to the rustworkx-core module ``rustworkx_core::generators`` + ``barabasi_albert_graph()`` which is used to generate a random graph + using Barabási–Albert preferential attachment to extend an input graph. diff --git a/rustworkx-core/src/generators/mod.rs b/rustworkx-core/src/generators/mod.rs index 1b1b82f80..a4d4cb2e1 100644 --- a/rustworkx-core/src/generators/mod.rs +++ b/rustworkx-core/src/generators/mod.rs @@ -56,6 +56,7 @@ pub use hexagonal_lattice_graph::hexagonal_lattice_graph; pub use lollipop_graph::lollipop_graph; pub use path_graph::path_graph; pub use petersen_graph::petersen_graph; +pub use random_graph::barabasi_albert_graph; pub use random_graph::gnm_random_graph; pub use random_graph::gnp_random_graph; pub use random_graph::random_geometric_graph; diff --git a/rustworkx-core/src/generators/random_graph.rs b/rustworkx-core/src/generators/random_graph.rs index 235adbb71..1df0b9bf0 100644 --- a/rustworkx-core/src/generators/random_graph.rs +++ b/rustworkx-core/src/generators/random_graph.rs @@ -15,12 +15,18 @@ use std::hash::Hash; use petgraph::data::{Build, Create}; -use petgraph::visit::{Data, EdgeRef, GraphBase, GraphProp, IntoEdgeReferences, NodeIndexable}; +use petgraph::visit::{ + Data, EdgeRef, GraphBase, GraphProp, IntoEdgeReferences, IntoEdgesDirected, + IntoNodeIdentifiers, NodeCount, NodeIndexable, +}; +use petgraph::{Incoming, Outgoing}; +use hashbrown::HashSet; use rand::distributions::{Distribution, Uniform}; use rand::prelude::*; use rand_pcg::Pcg64; +use super::star_graph; use super::InvalidInputError; /// Generate a Gnp random graph, also known as an @@ -395,10 +401,129 @@ where Ok(graph) } +/// Generate a random Barabási–Albert preferential attachment algorithm +/// +/// A graph of `n` nodes is grown by attaching new nodes each with `m` +/// edges that are preferentially attached to existing nodes with high degree. +/// If the graph is directed for the purposes of the extension algorithm all +/// edges are treated as weak (meaning both incoming and outgoing). +/// +/// The algorithm implemented in this function is described in: +/// +/// A. L. Barabási and R. Albert "Emergence of scaling in random networks", +/// Science 286, pp 509-512, 1999. +/// +/// Arguments: +/// +/// * `n` - The number of nodes to extend the graph to. +/// * `m` - The number of edges to attach from a new node to existing nodes. +/// * `seed` - An optional seed to use for the random number generator. +/// * `initial_graph` - An optional starting graph to expand, if not specified +/// a star graph of `m` nodes is generated and used. If specified the input +/// graph is mutated by this function and is expected to be moved into this +/// function. +/// * `default_node_weight` - A callable that will return the weight to use +/// for newly created nodes. +/// * `default_edge_weight` - A callable that will return the weight object +/// to use for newly created edges. +/// +/// An `InvalidInput` error is returned under the following conditions. If `m < 1` +/// or `m >= n` and if an `initial_graph` is specified and the number of nodes in +/// `initial_graph` is `< m` or `> n`. +/// +/// # Example +/// ```rust +/// use rustworkx_core::petgraph; +/// use rustworkx_core::generators::barabasi_albert_graph; +/// use rustworkx_core::generators::star_graph; +/// +/// let graph: petgraph::graph::UnGraph<(), ()> = barabasi_albert_graph( +/// 20, +/// 12, +/// Some(42), +/// None, +/// || {()}, +/// || {()}, +/// ).unwrap(); +/// assert_eq!(graph.node_count(), 20); +/// assert_eq!(graph.edge_count(), 107); +/// ``` +pub fn barabasi_albert_graph( + n: usize, + m: usize, + seed: Option, + initial_graph: Option, + mut default_node_weight: F, + mut default_edge_weight: H, +) -> Result +where + G: Data + + NodeIndexable + + GraphProp + + NodeCount + + Build + + Create, + for<'b> &'b G: GraphBase + IntoEdgesDirected + IntoNodeIdentifiers, + F: FnMut() -> T, + H: FnMut() -> M, + G::NodeId: Eq + Hash + std::fmt::Debug, +{ + if m < 1 || m >= n { + return Err(InvalidInputError {}); + } + let mut rng: Pcg64 = match seed { + Some(seed) => Pcg64::seed_from_u64(seed), + None => Pcg64::from_entropy(), + }; + let mut graph = match initial_graph { + Some(initial_graph) => initial_graph, + None => star_graph( + Some(m), + None, + &mut default_node_weight, + &mut default_edge_weight, + false, + false, + )?, + }; + if graph.node_count() < m || graph.node_count() > n { + return Err(InvalidInputError {}); + } + + let mut repeated_nodes: Vec = graph + .node_identifiers() + .flat_map(|x| { + let degree = graph + .edges_directed(x, Outgoing) + .chain(graph.edges_directed(x, Incoming)) + .count(); + std::iter::repeat(x).take(degree) + }) + .collect(); + let mut source = graph.node_count(); + while source < n { + let source_index = graph.add_node(default_node_weight()); + let mut targets: HashSet = HashSet::with_capacity(m); + while targets.len() < m { + targets.insert(*repeated_nodes.choose(&mut rng).unwrap()); + } + for target in &targets { + graph.add_edge(source_index, *target, default_edge_weight()); + } + repeated_nodes.extend(targets); + repeated_nodes.extend(vec![source_index; m]); + source += 1 + } + Ok(graph) +} + #[cfg(test)] mod tests { use crate::generators::InvalidInputError; - use crate::generators::{gnm_random_graph, gnp_random_graph, random_geometric_graph}; + use crate::generators::{ + barabasi_albert_graph, gnm_random_graph, gnp_random_graph, path_graph, + random_geometric_graph, + }; use crate::petgraph; // Test gnp_random_graph @@ -574,4 +699,54 @@ mod tests { Err(e) => assert_eq!(e, InvalidInputError), }; } + + #[test] + fn test_barabasi_albert_graph_starting_graph() { + let starting_graph: petgraph::graph::UnGraph<(), ()> = + path_graph(Some(40), None, || (), || (), false).unwrap(); + let graph = + barabasi_albert_graph(500, 40, None, Some(starting_graph), || (), || ()).unwrap(); + assert_eq!(graph.node_count(), 500); + assert_eq!(graph.edge_count(), 18439); + } + + #[test] + fn test_barabasi_albert_graph_invalid_starting_size() { + match barabasi_albert_graph( + 5, + 40, + None, + None::>, + || (), + || (), + ) { + Ok(_) => panic!("Returned a non-error"), + Err(e) => assert_eq!(e, InvalidInputError), + } + } + + #[test] + fn test_barabasi_albert_graph_invalid_equal_starting_size() { + match barabasi_albert_graph( + 5, + 5, + None, + None::>, + || (), + || (), + ) { + Ok(_) => panic!("Returned a non-error"), + Err(e) => assert_eq!(e, InvalidInputError), + } + } + + #[test] + fn test_barabasi_albert_graph_invalid_starting_graph() { + let starting_graph: petgraph::graph::UnGraph<(), ()> = + path_graph(Some(4), None, || (), || (), false).unwrap(); + match barabasi_albert_graph(500, 40, None, Some(starting_graph), || (), || ()) { + Ok(_) => panic!("Returned a non-error"), + Err(e) => assert_eq!(e, InvalidInputError), + } + } } diff --git a/src/lib.rs b/src/lib.rs index bae7d05d7..e078c9c35 100644 --- a/src/lib.rs +++ b/src/lib.rs @@ -454,6 +454,8 @@ fn rustworkx(py: Python<'_>, m: &PyModule) -> PyResult<()> { m.add_wrapped(wrap_pyfunction!(directed_gnm_random_graph))?; m.add_wrapped(wrap_pyfunction!(undirected_gnm_random_graph))?; m.add_wrapped(wrap_pyfunction!(random_geometric_graph))?; + m.add_wrapped(wrap_pyfunction!(barabasi_albert_graph))?; + m.add_wrapped(wrap_pyfunction!(directed_barabasi_albert_graph))?; m.add_wrapped(wrap_pyfunction!(cycle_basis))?; m.add_wrapped(wrap_pyfunction!(simple_cycles))?; m.add_wrapped(wrap_pyfunction!(strongly_connected_components))?; diff --git a/src/random_graph.rs b/src/random_graph.rs index c9e830686..60b1888a6 100644 --- a/src/random_graph.rs +++ b/src/random_graph.rs @@ -379,3 +379,125 @@ pub fn random_geometric_graph( }; Ok(graph) } + +/// Generate a random graph using Barabási–Albert preferential attachment +/// +/// A graph is grown to $n$ nodes by adding new nodes each with $m$ edges that +/// are preferentially attached to existing nodes with high degree. All the edges +/// and nodes added to this graph will have weights of ``None``. +/// +/// The algorithm performed by this function are described in: +/// +/// A. L. Barabási and R. Albert "Emergence of scaling in random networks", +/// Science 286, pp 509-512, 1999. +/// +/// :param int n: The number of nodes to extend the graph to. +/// :param int m: The number of edges to attach from a new node to existing nodes. +/// :param int seed: An optional seed to use for the random number generator +/// :param PyGraph initial_graph: An optional initial graph to use as a starting +/// point. :func:`.star_graph` is used to create an ``m`` node star graph +/// to use as a starting point. If specified the input graph will be +/// modified in place. +/// +/// :return: A PyGraph object +/// :rtype: PyGraph +#[pyfunction] +pub fn barabasi_albert_graph( + py: Python, + n: usize, + m: usize, + seed: Option, + initial_graph: Option, +) -> PyResult { + let default_fn = || py.None(); + if m < 1 { + return Err(PyValueError::new_err("m must be > 0")); + } + if m >= n { + return Err(PyValueError::new_err("m must be < n")); + } + let graph = match core_generators::barabasi_albert_graph( + n, + m, + seed, + initial_graph.map(|x| x.graph), + default_fn, + default_fn, + ) { + Ok(graph) => graph, + Err(_) => { + return Err(PyValueError::new_err( + "initial_graph has either less nodes than m, or more nodes than n", + )) + } + }; + Ok(graph::PyGraph { + graph, + node_removed: false, + multigraph: true, + attrs: py.None(), + }) +} + +/// Generate a random graph using Barabási–Albert preferential attachment +/// +/// A graph is grown to $n$ nodes by adding new nodes each with $m$ edges that +/// are preferentially attached to existing nodes with high degree. All the edges +/// and nodes added to this graph will have weights of ``None``. For the purposes +/// of the extension algorithm all edges are treated as weak (meaning directionality +/// isn't considered). +/// +/// The algorithm performed by this function are described in: +/// +/// A. L. Barabási and R. Albert "Emergence of scaling in random networks", +/// Science 286, pp 509-512, 1999. +/// +/// :param int n: The number of nodes to extend the graph to. +/// :param int m: The number of edges to attach from a new node to existing nodes. +/// :param int seed: An optional seed to use for the random number generator +/// :param PyDiGraph initial_graph: An optional initial graph to use as a starting +/// point. :func:`.star_graph` is used to create an ``m`` node star graph +/// to use as a starting point. If specified the input graph will be +/// modified in place. +/// +/// :return: A PyDiGraph object +/// :rtype: PyDiGraph +#[pyfunction] +pub fn directed_barabasi_albert_graph( + py: Python, + n: usize, + m: usize, + seed: Option, + initial_graph: Option, +) -> PyResult { + let default_fn = || py.None(); + if m < 1 { + return Err(PyValueError::new_err("m must be > 0")); + } + if m >= n { + return Err(PyValueError::new_err("m must be < n")); + } + let graph = match core_generators::barabasi_albert_graph( + n, + m, + seed, + initial_graph.map(|x| x.graph), + default_fn, + default_fn, + ) { + Ok(graph) => graph, + Err(_) => { + return Err(PyValueError::new_err( + "initial_graph has either less nodes than m, or more nodes than n", + )) + } + }; + Ok(digraph::PyDiGraph { + graph, + node_removed: false, + check_cycle: false, + cycle_state: algo::DfsSpace::default(), + multigraph: false, + attrs: py.None(), + }) +} diff --git a/tests/rustworkx_tests/test_random.py b/tests/rustworkx_tests/test_random.py index 1a96b27ef..949657864 100644 --- a/tests/rustworkx_tests/test_random.py +++ b/tests/rustworkx_tests/test_random.py @@ -236,3 +236,45 @@ def test_random_gnm_non_induced_subgraph_isomorphism(self): self.assertTrue( rustworkx.is_subgraph_isomorphic(graph, subgraph, id_order=True, induced=False) ) + + +class TestBarabasiAlbertGraph(unittest.TestCase): + def test_barabasi_albert_graph(self): + graph = rustworkx.barabasi_albert_graph(500, 450, 42) + self.assertEqual(graph.num_nodes(), 500) + self.assertEqual(graph.num_edges(), (50 * 450) + 449) + + def test_directed_barabasi_albert_graph(self): + graph = rustworkx.directed_barabasi_albert_graph(500, 450, 42) + self.assertEqual(graph.num_nodes(), 500) + self.assertEqual(graph.num_edges(), (50 * 450) + 449) + + def test_barabasi_albert_graph_with_starting_graph(self): + initial_graph = rustworkx.generators.path_graph(450) + graph = rustworkx.barabasi_albert_graph(500, 450, 42, initial_graph) + self.assertEqual(graph.num_nodes(), 500) + self.assertEqual(graph.num_edges(), (50 * 450) + 449) + + def test_directed_barabasi_albert_graph_with_starting_graph(self): + initial_graph = rustworkx.generators.directed_path_graph(450) + graph = rustworkx.directed_barabasi_albert_graph(500, 450, 42, initial_graph) + self.assertEqual(graph.num_nodes(), 500) + self.assertEqual(graph.num_edges(), (50 * 450) + 449) + + def test_invalid_barabasi_albert_graph_args(self): + with self.assertRaises(ValueError): + rustworkx.barabasi_albert_graph(5, 400) + with self.assertRaises(ValueError): + rustworkx.barabasi_albert_graph(5, 0) + initial_graph = rustworkx.generators.path_graph(450) + with self.assertRaises(ValueError): + rustworkx.barabasi_albert_graph(5, 4, initial_graph=initial_graph) + + def test_invalid_directed_barabasi_albert_graph_args(self): + with self.assertRaises(ValueError): + rustworkx.directed_barabasi_albert_graph(5, 400) + with self.assertRaises(ValueError): + rustworkx.directed_barabasi_albert_graph(5, 0) + initial_graph = rustworkx.generators.directed_path_graph(450) + with self.assertRaises(ValueError): + rustworkx.directed_barabasi_albert_graph(5, 4, initial_graph=initial_graph) From 537fc61e16c653d59551b97c007c9ee9595510b2 Mon Sep 17 00:00:00 2001 From: Matthew Treinish Date: Tue, 17 Oct 2023 10:49:15 -0400 Subject: [PATCH 2/3] Switch release note to use mpl_draw instead of graphviz_draw --- releasenotes/notes/add-albert-graph-5a7d393e1fe18e9d.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/releasenotes/notes/add-albert-graph-5a7d393e1fe18e9d.yaml b/releasenotes/notes/add-albert-graph-5a7d393e1fe18e9d.yaml index 5cb3f8e48..afe6052a0 100644 --- a/releasenotes/notes/add-albert-graph-5a7d393e1fe18e9d.yaml +++ b/releasenotes/notes/add-albert-graph-5a7d393e1fe18e9d.yaml @@ -9,11 +9,11 @@ features: .. jupyter-execute:: import rustworkx - from rustworkx.visualization import graphviz_draw + from rustworkx.visualization import mpl_draw starting_graph = rustworkx.generators.path_graph(10) random_graph = rustworkx.barabasi_albert_graph(20, 10, initial_graph=starting_graph) - graphviz_draw(random_graph, method="neato") + mpl_draw(random_graph) - | Added a new function to the rustworkx-core module ``rustworkx_core::generators`` ``barabasi_albert_graph()`` which is used to generate a random graph From 4bf0cb01fb7b2bed366ba3624a2fa0cfccebfe10 Mon Sep 17 00:00:00 2001 From: Matthew Treinish Date: Wed, 18 Oct 2023 18:46:28 -0400 Subject: [PATCH 3/3] Apply suggestions from code review Co-authored-by: Edwin Navarro --- rustworkx-core/src/generators/random_graph.rs | 2 +- src/random_graph.rs | 6 +++--- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/rustworkx-core/src/generators/random_graph.rs b/rustworkx-core/src/generators/random_graph.rs index 1df0b9bf0..a58080ec2 100644 --- a/rustworkx-core/src/generators/random_graph.rs +++ b/rustworkx-core/src/generators/random_graph.rs @@ -466,7 +466,7 @@ where for<'b> &'b G: GraphBase + IntoEdgesDirected + IntoNodeIdentifiers, F: FnMut() -> T, H: FnMut() -> M, - G::NodeId: Eq + Hash + std::fmt::Debug, + G::NodeId: Eq + Hash, { if m < 1 || m >= n { return Err(InvalidInputError {}); diff --git a/src/random_graph.rs b/src/random_graph.rs index 60b1888a6..d15aeeaa2 100644 --- a/src/random_graph.rs +++ b/src/random_graph.rs @@ -386,14 +386,14 @@ pub fn random_geometric_graph( /// are preferentially attached to existing nodes with high degree. All the edges /// and nodes added to this graph will have weights of ``None``. /// -/// The algorithm performed by this function are described in: +/// The algorithm performed by this function is described in: /// /// A. L. Barabási and R. Albert "Emergence of scaling in random networks", /// Science 286, pp 509-512, 1999. /// /// :param int n: The number of nodes to extend the graph to. /// :param int m: The number of edges to attach from a new node to existing nodes. -/// :param int seed: An optional seed to use for the random number generator +/// :param int seed: An optional seed to use for the random number generator. /// :param PyGraph initial_graph: An optional initial graph to use as a starting /// point. :func:`.star_graph` is used to create an ``m`` node star graph /// to use as a starting point. If specified the input graph will be @@ -447,7 +447,7 @@ pub fn barabasi_albert_graph( /// of the extension algorithm all edges are treated as weak (meaning directionality /// isn't considered). /// -/// The algorithm performed by this function are described in: +/// The algorithm performed by this function is described in: /// /// A. L. Barabási and R. Albert "Emergence of scaling in random networks", /// Science 286, pp 509-512, 1999.