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//! Submodule providing the `LineGraph` trait for computing line graphs.
//!
//! Given a graph G, the line graph L(G) has one vertex per edge of G.
//! Two vertices in L(G) are adjacent iff the corresponding edges in G
//! share an endpoint.
use alloc::vec::Vec;
use num_traits::AsPrimitive;
use crate::{
impls::{CSR2D, SquareCSR2D, SymmetricCSR2D},
traits::MonoplexMonopartiteGraph,
};
/// Result of computing a line graph L(G).
///
/// Contains the line graph itself and a mapping from each vertex in L(G)
/// back to the original edge (src, dst) in G.
#[derive(Clone, Debug, PartialEq, Eq, Hash)]
pub struct LineGraphResult<M, NodeId> {
graph: M,
edge_map: Vec<(NodeId, NodeId)>,
}
impl<M, NodeId: Copy> LineGraphResult<M, NodeId> {
/// Creates a new `LineGraphResult`.
#[inline]
pub fn new(graph: M, edge_map: Vec<(NodeId, NodeId)>) -> Self {
Self { graph, edge_map }
}
/// Returns a reference to the line graph.
#[inline]
pub fn graph(&self) -> &M {
&self.graph
}
/// Consumes the result and returns the line graph.
#[inline]
pub fn into_graph(self) -> M {
self.graph
}
/// Returns the mapping from line graph vertices to original edges.
#[inline]
pub fn edge_map(&self) -> &[(NodeId, NodeId)] {
&self.edge_map
}
/// Returns the number of vertices in the line graph.
#[inline]
pub fn number_of_vertices(&self) -> usize {
self.edge_map.len()
}
/// Returns the original edge corresponding to a vertex in the line graph.
#[inline]
pub fn original_edge(&self, vertex: usize) -> (NodeId, NodeId) {
self.edge_map[vertex]
}
}
/// Trait providing line graph construction for monoplex monopartite graphs.
pub trait LineGraph: MonoplexMonopartiteGraph {
/// Computes the undirected line graph L(G).
///
/// Edges are filtered to `src < dst`, which naturally excludes self-loops
/// (standard for undirected simple graphs per Krausz 1943, Beineke 1968).
///
/// # Complexity
/// O(sum of deg(v)^2) time + O(|E(L(G))| log |E(L(G))|) for sorting.
fn line_graph(
&self,
) -> LineGraphResult<SymmetricCSR2D<CSR2D<usize, usize, usize>>, Self::NodeId>;
/// Computes the directed line digraph L(G).
///
/// Edge i is adjacent to edge j in L(G) iff the head of i equals the tail
/// of j. Self-loops in G are preserved per Hemminger & Beineke (1978).
///
/// # Complexity
/// O(sum of in_deg(v) * out_deg(v)) time + sorting.
fn directed_line_graph(
&self,
) -> LineGraphResult<SquareCSR2D<CSR2D<usize, usize, usize>>, Self::NodeId>;
}
impl<G: MonoplexMonopartiteGraph> LineGraph for G
where
G::NodeId: AsPrimitive<usize>,
{
fn line_graph(
&self,
) -> LineGraphResult<SymmetricCSR2D<CSR2D<usize, usize, usize>>, Self::NodeId> {
let n: usize = self.number_of_nodes().as_();
// Step 1: Collect edges with src < dst (undirected, no self-loops).
let edge_map: Vec<(Self::NodeId, Self::NodeId)> =
self.sparse_coordinates().filter(|(src, dst)| src < dst).collect();
let m = edge_map.len();
if m == 0 {
let graph =
crate::traits::algorithms::randomized_graphs::builder_utils::build_symmetric(
0,
Vec::new(),
);
return LineGraphResult { graph, edge_map };
}
// Step 2: Build incidence lists — for each vertex, the list of edge
// indices.
let mut incident: Vec<Vec<usize>> = vec![Vec::new(); n];
for (idx, &(src, dst)) in edge_map.iter().enumerate() {
incident[src.as_()].push(idx);
incident[dst.as_()].push(idx);
}
// Step 3: For each vertex, connect all pairs of incident edges.
let mut lg_edges: Vec<(usize, usize)> = Vec::new();
for inc in &incident {
let len = inc.len();
for i in 0..len {
for j in (i + 1)..len {
lg_edges.push((inc[i], inc[j]));
}
}
}
// Step 4: Sort and deduplicate, then build.
lg_edges.sort_unstable();
lg_edges.dedup();
let graph = crate::traits::algorithms::randomized_graphs::builder_utils::build_symmetric(
m, lg_edges,
);
LineGraphResult { graph, edge_map }
}
fn directed_line_graph(
&self,
) -> LineGraphResult<SquareCSR2D<CSR2D<usize, usize, usize>>, Self::NodeId> {
let n: usize = self.number_of_nodes().as_();
// Step 1: Collect all edges (including self-loops).
let edge_map: Vec<(Self::NodeId, Self::NodeId)> = self.sparse_coordinates().collect();
let m = edge_map.len();
if m == 0 {
let graph = crate::traits::algorithms::randomized_graphs::builder_utils::build_directed(
0,
Vec::new(),
);
return LineGraphResult { graph, edge_map };
}
// Step 2: Build outgoing[v] and incoming[v] edge index lists.
let mut outgoing: Vec<Vec<usize>> = vec![Vec::new(); n];
let mut incoming: Vec<Vec<usize>> = vec![Vec::new(); n];
for (idx, &(src, dst)) in edge_map.iter().enumerate() {
outgoing[src.as_()].push(idx);
incoming[dst.as_()].push(idx);
}
// Step 3: For each vertex v, connect every incoming edge to every
// outgoing edge.
let mut lg_edges: Vec<(usize, usize)> = Vec::new();
for v in 0..n {
for &i in &incoming[v] {
for &j in &outgoing[v] {
lg_edges.push((i, j));
}
}
}
// Step 4: Sort and build.
lg_edges.sort_unstable();
let graph = crate::traits::algorithms::randomized_graphs::builder_utils::build_directed(
m, lg_edges,
);
LineGraphResult { graph, edge_map }
}
}