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Add chain_decomposition function. #444

Merged
merged 14 commits into from
Oct 15, 2021
1 change: 1 addition & 0 deletions docs/source/api.rst
Original file line number Diff line number Diff line change
Expand Up @@ -130,6 +130,7 @@ Connectivity and Cycles
retworkx.is_weakly_connected
retworkx.cycle_basis
retworkx.digraph_find_cycle
retworkx.chain_decomposition

.. _other-algorithms:

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40 changes: 40 additions & 0 deletions releasenotes/notes/chain-decomposition-3fdf4b283b5b9ad1.yaml
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@@ -0,0 +1,40 @@
---
features:
- |
Added a new function :func:`~retworkx.chain_decomposition` that finds
a chain decomposition of an undirected :class:`~retworkx.PyGraph`.
A chain decomposition is a set of cycles or paths derived from the
set of fundamental cycles of a depth-first tree. It's defined in
https://doi.org/10.1016/j.ipl.2013.01.016
For example:

.. jupyter-execute::

import retworkx
from retworkx.visualization import mpl_draw

graph = retworkx.PyGraph()
graph.extend_from_edge_list([
(0, 1), (0, 2), (1, 2), (2, 3),
(3, 4), (3, 5), (4, 5),
])
chains = retworkx.chain_decomposition(graph)

def color_edges(graph, chains):
COLORS = ['blue', 'red']

edges_in_chain = {}
for idx, chain in enumerate(chains):
for edge in chain:
edge = tuple(sorted(edge))
edges_in_chain[edge] = COLORS[idx]

edge_colors = []
for edge in graph.edge_list():
edge = tuple(sorted(edge))
edge_colors += [edges_in_chain.get(edge, 'black')]

return edge_colors

mpl_draw(graph, node_color='black', node_size=150,
edge_color=color_edges(graph, chains))
172 changes: 172 additions & 0 deletions retworkx-core/src/connectivity/chain.rs
Original file line number Diff line number Diff line change
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// Licensed under the Apache License, Version 2.0 (the "License"); you may
// not use this file except in compliance with the License. You may obtain
// a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
// WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
// License for the specific language governing permissions and limitations
// under the License.

use std::cmp::Eq;
use std::hash::Hash;

use hashbrown::HashMap;

use petgraph::visit::{
depth_first_search, DfsEvent, GraphProp, IntoNeighbors,
IntoNodeIdentifiers, NodeCount, NodeIndexable, VisitMap, Visitable,
};
use petgraph::Undirected;

fn _build_chain<G, VM: VisitMap<G::NodeId>>(
graph: G,
parent: &[usize],
mut u_id: G::NodeId,
mut v_id: G::NodeId,
visited: &mut VM,
) -> Vec<(G::NodeId, G::NodeId)>
where
G: Visitable + NodeIndexable,
{
let mut chain = Vec::new();
while visited.visit(v_id) {
chain.push((u_id, v_id));
u_id = v_id;
let u = graph.to_index(u_id);
let v = parent[u];
v_id = graph.from_index(v);
}
chain.push((u_id, v_id));

chain
}

/// Returns the chain decomposition of a graph.
///
/// The *chain decomposition* of a graph with respect to a depth-first
/// search tree is a set of cycles or paths derived from the set of
/// fundamental cycles of the tree in the following manner. Consider
/// each fundamental cycle with respect to the given tree, represented
/// as a list of edges beginning with the nontree edge oriented away
/// from the root of the tree. For each fundamental cycle, if it
/// overlaps with any previous fundamental cycle, just take the initial
/// non-overlapping segment, which is a path instead of a cycle. Each
/// cycle or path is called a *chain*. For more information,
/// see [`Jens`](https://doi.org/10.1016/j.ipl.2013.01.016).
///
/// The graph should be undirected. If `source` is specified only the chain
/// decomposition for the connected component containing this node will be returned.
/// This node indicates the root of the depth-first search tree. If it's not
/// specified, a source will be chosen arbitrarly and repeated until all components
/// of the graph are searched.
///
/// Returns a list of list of edges where each inner list is a chain.
///
/// # Note
/// The function implicitly assumes that there are no parallel edges
/// or self loops. It may produce incorrect/unexpected results if the
/// input graph has self loops or parallel edges.
///
/// # Example
/// ```rust
/// use retworkx_core::connectivity::chain_decomposition;
/// use retworkx_core::petgraph::graph::{NodeIndex, UnGraph};
///
/// let mut graph : UnGraph<(), ()> = UnGraph::new_undirected();
/// let a = graph.add_node(()); // node with no weight
/// let b = graph.add_node(());
/// let c = graph.add_node(());
/// let d = graph.add_node(());
/// let e = graph.add_node(());
/// let f = graph.add_node(());
/// let g = graph.add_node(());
/// let h = graph.add_node(());
///
/// graph.extend_with_edges(&[
/// (a, b),
/// (b, c),
/// (c, d),
/// (d, a),
/// (e, f),
/// (b, e),
/// (f, g),
/// (g, h),
/// (h, e)
/// ]);
/// // a ---- b ---- e ---- f
/// // | | | |
/// // d ---- c h ---- g
///
/// let chains = chain_decomposition(&graph, None);
/// assert_eq!(
/// chains,
/// vec![
/// vec![(a, d), (d, c), (c, b), (b, a)],
/// vec![(e, h), (h, g), (g, f), (f, e)]
/// ]
/// );
/// ```
pub fn chain_decomposition<G>(
graph: G,
source: Option<G::NodeId>,
) -> Vec<Vec<(G::NodeId, G::NodeId)>>
where
G: IntoNodeIdentifiers
+ IntoNeighbors
+ Visitable
+ NodeIndexable
+ NodeCount
+ GraphProp<EdgeType = Undirected>,
G::NodeId: Eq + Hash,
{
let roots = match source {
Some(node) => vec![node],
None => graph.node_identifiers().collect(),
};

let mut parent = vec![std::usize::MAX; graph.node_bound()];
let mut back_edges: HashMap<G::NodeId, Vec<G::NodeId>> = HashMap::new();

// depth-first-index (DFI) ordered nodes.
let mut nodes = Vec::with_capacity(graph.node_count());
depth_first_search(graph, roots, |event| match event {
DfsEvent::Discover(u, _) => {
nodes.push(u);
}
DfsEvent::TreeEdge(u, v) => {
let u = graph.to_index(u);
let v = graph.to_index(v);
parent[v] = u;
}
DfsEvent::BackEdge(u_id, v_id) => {
let u = graph.to_index(u_id);
let v = graph.to_index(v_id);

// do *not* consider ``(u, v)`` as a back edge if ``(v, u)`` is a tree edge.
if parent[u] != v {
back_edges
.entry(v_id)
.and_modify(|v_edges| v_edges.push(u_id))
.or_insert(vec![u_id]);
}
}
_ => {}
});

let visited = &mut graph.visit_map();
nodes
.into_iter()
.filter_map(|u| {
visited.visit(u);
back_edges.get(&u).map(|vs| {
vs.iter()
.map(|v| _build_chain(graph, &parent, u, *v, visited))
.collect::<Vec<Vec<(G::NodeId, G::NodeId)>>>()
})
})
.flatten()
.collect()
}
17 changes: 17 additions & 0 deletions retworkx-core/src/connectivity/mod.rs
Original file line number Diff line number Diff line change
@@ -0,0 +1,17 @@
// Licensed under the Apache License, Version 2.0 (the "License"); you may
// not use this file except in compliance with the License. You may obtain
// a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
// WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
// License for the specific language governing permissions and limitations
// under the License.

//! Module for connectivity and cut algorithms.

mod chain;

pub use chain::chain_decomposition;
1 change: 1 addition & 0 deletions retworkx-core/src/lib.rs
Original file line number Diff line number Diff line change
Expand Up @@ -68,6 +68,7 @@ pub type Result<T, E = Infallible> = core::result::Result<T, E>;

/// Module for centrality algorithms
pub mod centrality;
pub mod connectivity;
/// Module for depth first search edge methods
pub mod dfs_edges;
/// Module for maximum weight matching algorithmss
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53 changes: 53 additions & 0 deletions src/connectivity/mod.rs
Original file line number Diff line number Diff line change
Expand Up @@ -34,6 +34,7 @@ use ndarray::prelude::*;
use numpy::IntoPyArray;

use crate::iterators::EdgeList;
use retworkx_core::connectivity;

/// Return a list of cycles which form a basis for cycles of a given PyGraph
///
Expand Down Expand Up @@ -662,3 +663,55 @@ pub fn digraph_core_number(
) -> PyResult<PyObject> {
core_number::core_number(py, &graph.graph)
}

/// Returns the chain decomposition of a graph.
///
/// The *chain decomposition* of a graph with respect to a depth-first
/// search tree is a set of cycles or paths derived from the set of
/// fundamental cycles of the tree in the following manner. Consider
/// each fundamental cycle with respect to the given tree, represented
/// as a list of edges beginning with the nontree edge oriented away
/// from the root of the tree. For each fundamental cycle, if it
/// overlaps with any previous fundamental cycle, just take the initial
/// non-overlapping segment, which is a path instead of a cycle. Each
/// cycle or path is called a *chain*. For more information, see [Jens]_.
///
/// .. note::
///
/// The function implicitly assumes that there are no parallel edges
/// or self loops. It may produce incorrect/unexpected results if the
/// input graph has self loops or parallel edges. It's also a recursive
/// implementation and might run out of memory in large graphs.
///
/// :param PyGraph: The undirected graph to be used
/// :param int source: An optional node index in the graph. If specified,
/// only the chain decomposition for the connected component containing
/// this node will be returned. This node indicates the root of the depth-first
/// search tree. If this is not specified then a source will be chosen
/// arbitrarly and repeated until all components of the graph are searched.
/// :returns: A list of list of edges where each inner list is a chain.
/// :rtype: list of EdgeList
///
/// .. [Jens] Jens M. Schmidt (2013). "A simple test on 2-vertex-
/// and 2-edge-connectivity." *Information Processing Letters*,
/// 113, 241–244. Elsevier. <https://doi.org/10.1016/j.ipl.2013.01.016>
#[pyfunction]
#[pyo3(text_signature = "(graph, /, source=None)")]
pub fn chain_decomposition(
graph: graph::PyGraph,
source: Option<usize>,
) -> Vec<EdgeList> {
let chains = connectivity::chain_decomposition(
&graph.graph,
source.map(NodeIndex::new),
);
chains
.into_iter()
.map(|chain| EdgeList {
edges: chain
.into_iter()
.map(|(a, b)| (a.index(), b.index()))
.collect(),
})
.collect()
}
1 change: 1 addition & 0 deletions src/lib.rs
Original file line number Diff line number Diff line change
Expand Up @@ -301,6 +301,7 @@ fn retworkx(py: Python<'_>, m: &PyModule) -> PyResult<()> {
))?;
m.add_wrapped(wrap_pyfunction!(metric_closure))?;
m.add_wrapped(wrap_pyfunction!(steiner_tree))?;
m.add_wrapped(wrap_pyfunction!(chain_decomposition))?;
m.add_class::<digraph::PyDiGraph>()?;
m.add_class::<graph::PyGraph>()?;
m.add_class::<iterators::BFSSuccessors>()?;
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