bixverse_methods/graph/

graph_structures.rs

//! Structures for graphs within bixverse.

#![allow(dead_code)]

use faer::{Mat, MatRef};
use rayon::prelude::*;
use rustc_hash::FxHashMap;
use std::clone::Clone;
use std::collections::BinaryHeap;

use crate::prelude::*;
use bixverse_core::core::math::sparse::coo_to_csr;

////////////
// Consts //
////////////

/// Rows of the similarity matrix processed per parallel task in the kNN build
const KNN_ROW_BLOCK: usize = 32;

/// Tile edge for the symmetrisation pass
const KNN_SYM_TILE: usize = 64;

///////////////
// Union-find //
///////////////

/// Disjoint-set forest with path halving and union by size.
pub struct UnionFind {
    /// Parent pointer per element; a root points at itself.
    parent: Vec<u32>,
    /// Size of the tree rooted at each element. Only meaningful at roots.
    size: Vec<u32>,
}

impl UnionFind {
    /// Create `n` singleton sets.
    ///
    /// ### Params
    ///
    /// * `n` - Number of elements.
    ///
    /// ### Returns
    ///
    /// The initialised forest.
    pub fn new(n: usize) -> Self {
        Self {
            parent: (0..n as u32).collect(),
            size: vec![1; n],
        }
    }

    /// Find the root of `x`, halving the path on the way up.
    ///
    /// ### Params
    ///
    /// * `x` - Element to look up.
    ///
    /// ### Returns
    ///
    /// The root of `x`'s set.
    pub fn find(&mut self, mut x: u32) -> u32 {
        while self.parent[x as usize] != x {
            let grandparent = self.parent[self.parent[x as usize] as usize];
            self.parent[x as usize] = grandparent;
            x = grandparent;
        }
        x
    }

    /// Merge the sets containing `a` and `b`, smaller tree under larger.
    ///
    /// ### Params
    ///
    /// * `a` - First element.
    /// * `b` - Second element.
    ///
    /// ### Returns
    ///
    /// `true` when the two were in different sets and have just been merged,
    /// `false` when they already shared a root. Kruskal needs that answer to
    /// decide whether an edge joins two components or closes a cycle.
    pub fn union(&mut self, a: u32, b: u32) -> bool {
        let (mut ra, mut rb) = (self.find(a), self.find(b));
        if ra == rb {
            return false;
        }
        if self.size[ra as usize] < self.size[rb as usize] {
            std::mem::swap(&mut ra, &mut rb);
        }
        self.parent[rb as usize] = ra;
        self.size[ra as usize] += self.size[rb as usize];
        true
    }
}

/////////////////////////////
// Sparse graph structures //
/////////////////////////////

/// Structure representation of a sparse graph
///
/// ### Fields
///
/// * `adjacency` - Sparse CSR representation of the graph with `f16` as
///   weights.
/// * `num_nodes` - Number of nodes represented in the graph.
/// * `directed` - Whether the graph is directed or not.
#[derive(Clone, Debug)]
pub struct SparseGraph<T: Clone> {
    /// Adjacency matrix in CSR (symmetric for undirected graphs)
    adjacency: CompressedSparseData2<T>,
    num_nodes: usize,
    directed: bool,
}

impl<T> SparseGraph<T>
where
    T: Clone + BixverseFloat + std::iter::Sum,
{
    /// Generate a new SparseGraph
    ///
    /// ### Params
    ///
    /// * `num_nodes` - Number of nodes in the graph
    /// * `adjacency` - CompressedSparseData2 containing the adjacency
    ///   information
    /// * `directed` - Is this a directed or undirected graph
    ///
    /// ### Returns
    ///
    /// Initialised self
    pub fn new(num_nodes: usize, adjacency: CompressedSparseData2<T>, directed: bool) -> Self {
        Self {
            adjacency,
            num_nodes,
            directed,
        }
    }

    /// Helper function to get the neighbours and weights
    ///
    /// ### Params
    ///
    /// * `node` - Index of the node for which to get the neighbours
    ///
    /// ### Return
    ///
    /// Tuple of `(neighbour_indices, edge_weights)`
    #[inline]
    pub fn get_neighbours(&self, node: usize) -> (Vec<usize>, &[T]) {
        let start = self.adjacency.indptr[node] as usize;
        let end = self.adjacency.indptr[node + 1] as usize;
        (
            self.adjacency.indices[start..end].index_cast(),
            &self.adjacency.data[start..end],
        )
    }

    /// Get the neighbours and weights without allocating
    ///
    /// ### Params
    ///
    /// * `node` - Index of the node for which to get the neighbours
    ///
    /// ### Return
    ///
    /// Tuple of `(neighbour_indices, edge_weights)`, borrowed from the CSR
    #[inline]
    pub fn get_neighbours_raw(&self, node: usize) -> (&[u32], &[T]) {
        let start = self.adjacency.indptr[node] as usize;
        let end = self.adjacency.indptr[node + 1] as usize;
        (
            &self.adjacency.indices[start..end],
            &self.adjacency.data[start..end],
        )
    }

    /// Get the node degree
    ///
    /// ### Params
    ///
    /// * `node` - Index of the node to get the node degree from
    ///
    /// ### Return
    ///
    /// The node degree for this node.
    #[inline]
    pub fn get_node_degree(&self, node: usize) -> usize {
        (self.adjacency.indptr[node + 1] - self.adjacency.indptr[node]) as usize
    }

    /// Get total weight
    ///
    /// ### Params
    ///
    /// * `undirected` - Is the graph undirected or not.
    ///
    /// ### Returns
    ///
    /// The total edge weight
    pub fn total_weight(&self) -> T {
        let mut total = T::zero();
        for i in 0..self.num_nodes {
            let (_, weights) = self.get_neighbours(i);
            total += weights.iter().copied().sum::<T>();
        }
        let half = T::from_f64(0.5).unwrap();
        if !self.directed { total * half } else { total }
    }

    /// Expose the number of nodes
    ///
    /// ### Returns
    ///
    /// The number of nodes in the graph
    pub fn get_node_number(&self) -> usize {
        self.num_nodes
    }

    /// Expose if graph is directed
    ///
    /// ### Returns
    ///
    /// Boolean indicating if graph is directed
    pub fn is_directed(&self) -> bool {
        self.directed
    }
}

//////////////////////////////////
// Hierachical graph structures //
//////////////////////////////////

/// Represents one level in the hierarchy
///
/// ### Fields
///
/// * `graph` - The sparse graph
/// * `node_map` - Maps fine node -> coarse node
/// * `coarse_weights` - Number of fine nodes that mapped to each coarse node
#[allow(dead_code)]
pub struct CoarseLevel<T>
where
    T: Clone,
{
    graph: SparseGraph<T>,
    node_map: Vec<usize>,
    coarse_weights: Vec<usize>,
}

/// Multi-level graph hierarchy
///
/// ### Fields
///
/// * `finest` - Original finest level graph
/// * `levels` - Coarse levels
pub struct GraphHierarchy<T>
where
    T: Clone,
{
    finest: SparseGraph<T>,
    levels: Vec<CoarseLevel<T>>,
}

impl<T> GraphHierarchy<T>
where
    T: Clone + BixverseFloat + std::iter::Sum + std::default::Default,
{
    /// Build a Graph hierarchy based on an initial graph
    ///
    /// This one assumes undirected graphs!!!
    ///
    /// ### Params
    ///
    /// * `graph` - The initial SparseGraph
    /// * `reduction_ratio` - By how much to reduce per level the number of
    ///   nodes. Typically 0.5.
    /// * `max_levels` - Maximum depth in terms of coarsion.
    ///
    /// ### Returns
    ///
    /// Initialised self.
    pub fn build(graph: SparseGraph<T>, reduction_ratio: T, max_levels: usize) -> Self {
        assert!(
            !graph.is_directed(),
            "This is implemented for undirected graphs!"
        );
        let mut levels = Vec::new();
        let mut current_graph = graph.clone();

        let min_nodes = T::from_usize(100).unwrap();

        for _ in 0..max_levels {
            let coarse_level = Self::coarsen_level(&current_graph);

            let current_nodes = T::from_usize(current_graph.num_nodes).unwrap();
            let coarse_nodes = T::from_usize(coarse_level.graph.num_nodes).unwrap();
            let reduction = coarse_nodes / current_nodes;

            if reduction > reduction_ratio || coarse_nodes < min_nodes {
                break;
            }

            current_graph = coarse_level.graph.clone();
            levels.push(coarse_level);
        }

        Self {
            finest: graph,
            levels,
        }
    }

    /// Coarsen one level using heavy-edge matching
    ///
    /// Generates a CoarseLevel of a given graph via heavy edge matching.
    ///
    /// ### Params
    ///
    /// * `graph` - The SparseGraph to coarsen
    ///
    /// ### Returns
    ///
    /// CoarseLevel containing the coarsened graph and mapping information
    fn coarsen_level(graph: &SparseGraph<T>) -> CoarseLevel<T> {
        let matching = Self::heavy_edge_matching(graph);
        let (coarse_graph, node_map, coarse_weights) = Self::contract_graph(graph, &matching);

        CoarseLevel {
            graph: coarse_graph,
            node_map,
            coarse_weights,
        }
    }

    /// Match nodes via heavy edges
    ///
    /// Finds pairs of nodes to match based on heaviest edge weights. Each node
    /// is matched with its heaviest unmatched neighbour.
    ///
    /// ### Params
    ///
    /// * `graph` - SparseGraph for which to identify the matching nodes
    ///
    /// ### Returns
    ///
    /// Vector where each element is either None (unmatched) or Some(node_id)
    /// indicating which node this node is matched with
    fn heavy_edge_matching(graph: &SparseGraph<T>) -> Vec<Option<usize>> {
        let n = graph.num_nodes;
        let mut matching = vec![None; n];
        let mut matched = vec![false; n];

        for i in 0..n {
            if matched[i] {
                continue;
            }

            let (neighbour, weights) = graph.get_neighbours(i);

            // Find heaviest unmatched neighbour
            let mut best_neighbour = None;
            let mut best_weight = T::zero();

            for (&j, &w) in neighbour.iter().zip(weights.iter()) {
                if !matched[j] && j != i && w > best_weight {
                    best_weight = w;
                    best_neighbour = Some(j)
                }
            }

            if let Some(j) = best_neighbour {
                matching[i] = Some(j);
                matching[j] = Some(i);
                matched[i] = true;
                matched[j] = true;
            }
        }

        matching
    }

    /// Contract graph based on matching
    ///
    /// Creates a coarser graph by merging matched nodes. Edges between matched
    /// nodes are removed, and edges to external nodes are combined.
    ///
    /// ### Params
    ///
    /// * `graph` - The fine-level graph to contract
    /// * `matching` - Node matching information from heavy_edge_matching
    ///
    /// ### Returns
    ///
    /// Tuple of (coarse_graph, node_map, coarse_weights) where node_map
    /// indicates which coarse node each fine node maps to, and coarse_weights
    /// counts how many fine nodes were merged into each coarse node
    fn contract_graph(
        graph: &SparseGraph<T>,
        matching: &[Option<usize>],
    ) -> (SparseGraph<T>, Vec<usize>, Vec<usize>) {
        let n = graph.num_nodes;
        let mut node_map = vec![0; n];
        let mut coarse_id = 0;

        // assign coarse node IDs
        for i in 0..n {
            if node_map[i] == 0 || i == 0 {
                match matching[i] {
                    Some(j) if j > i => {
                        node_map[i] = coarse_id;
                        node_map[j] = coarse_id;
                        coarse_id += 1;
                    }
                    None => {
                        // i unchanged
                        node_map[i] = coarse_id;
                        coarse_id += 1;
                    }
                    _ => {} // already assigned
                }
            }
        }

        let num_coarse = coarse_id;
        let mut coarse_weights = vec![0; num_coarse];

        for i in 0..n {
            coarse_weights[node_map[i]] += 1;
        }

        // Build coarse graph edges
        let mut rows = Vec::new();
        let mut cols = Vec::new();
        let mut vals = Vec::new();

        for i in 0..n {
            let ci = node_map[i];
            let (neighbours, weights) = graph.get_neighbours(i);

            for (&j, &w) in neighbours.iter().zip(weights.iter()) {
                let cj = node_map[j];
                if ci != cj {
                    rows.push(ci);
                    cols.push(cj);
                    vals.push(w);
                }
            }
        }

        // Sort to group duplicates
        let mut edges: Vec<(usize, usize, T)> = rows
            .into_iter()
            .zip(cols)
            .zip(vals)
            .map(|((r, c), v)| (r, c, v))
            .collect();
        edges.sort_unstable_by_key(|(r, c, _)| (*r, *c));

        // Sum duplicates
        let mut deduped_rows = Vec::new();
        let mut deduped_cols = Vec::new();
        let mut deduped_vals = Vec::new();

        if !edges.is_empty() {
            let (mut curr_r, mut curr_c, mut curr_v) = edges[0];

            for &(r, c, v) in &edges[1..] {
                if r == curr_r && c == curr_c {
                    curr_v += v;
                } else {
                    deduped_rows.push(curr_r);
                    deduped_cols.push(curr_c);
                    deduped_vals.push(curr_v);
                    (curr_r, curr_c, curr_v) = (r, c, v);
                }
            }
            deduped_rows.push(curr_r);
            deduped_cols.push(curr_c);
            deduped_vals.push(curr_v);
        }

        let coarse_csr = coo_to_csr(
            &deduped_rows.index_cast(),
            &deduped_cols.index_cast(),
            &deduped_vals,
            (num_coarse, num_coarse),
        );

        let coarse_graph = SparseGraph::new(num_coarse, coarse_csr, false);

        (coarse_graph, node_map, coarse_weights)
    }

    /// Project coarse solution to fine level
    ///
    /// ### Params
    ///
    /// * `level` - Which coarse level (0 = first coarsening)
    /// * `coarse_labels` - Labels at coarse level
    ///
    /// ### Returns
    ///
    /// Vector of labels at the fine level
    pub fn prolong(&self, level: usize, coarse_labels: &[usize]) -> Vec<usize> {
        if level >= self.levels.len() {
            return coarse_labels.to_vec();
        }

        let node_map = &self.levels[level].node_map;
        node_map.iter().map(|&ci| coarse_labels[ci]).collect()
    }

    /// Get coarsest graph
    ///
    /// ### Returns
    ///
    /// Reference to the coarsest graph in the hierarchy
    pub fn coarsest(&self) -> &SparseGraph<T> {
        self.levels.last().map(|l| &l.graph).unwrap_or(&self.finest)
    }
}

/////////////////////////
// Heterogenous graphs //
/////////////////////////

/// Directed edge list with a type per node and a type and weight per edge,
/// for constrained personalised PageRank.
#[derive(Debug, Clone)]
pub struct AttributedGraph<'a, T> {
    /// Type per node
    pub node_types: Vec<&'a str>,
    /// 0-based source node per edge
    pub from: Vec<usize>,
    /// 0-based target node per edge
    pub to: Vec<usize>,
    /// Type per edge
    pub edge_types: Vec<&'a str>,
    /// Weight per edge
    pub weights: Vec<T>,
}

/// Generate a weighted, typed graph from node and edge lists
///
/// ### Params
///
/// * `nodes` - Slice of node names.
/// * `node_types` - Slice of the node types.
/// * `from` - Slice of the names of the from nodes.
/// * `to` - Slice of the names of the to nodes.
/// * `edge_types` - Slice of the edge types.
/// * `edge_weights` - Slice of the edge weights.
///
/// ### Returns
///
/// An `AttributedGraph` for subsequent usage in constrained personalised
/// page-rank iteration.
///
/// ### Panics
///
/// Function will panic if `nodes` and `nodes_types` do not have the same
/// length, when `from`, `to`, `edge_types` and `edge_weights` do not have the
/// same length, or when an edge names a node that is not in `nodes`.
pub fn graph_from_strings_with_attributes<'a, T>(
    nodes: &'a [String],
    node_types: &'a [String],
    from: &'a [String],
    to: &'a [String],
    edge_types: &'a [String],
    edge_weights: &'a [T],
) -> AttributedGraph<'a, T>
where
    T: BixverseFloat,
{
    assert_same_len!(nodes, node_types);
    assert_same_len!(from, to, edge_types, edge_weights);

    let name_to_idx: FxHashMap<&String, usize> = nodes
        .iter()
        .enumerate()
        .map(|(idx, name)| (name, idx))
        .collect();
    let lookup = |name: &String| {
        *name_to_idx
            .get(name)
            .unwrap_or_else(|| panic!("Node '{name}' not found in nodes list"))
    };

    AttributedGraph {
        node_types: node_types.iter().map(String::as_str).collect(),
        from: from.iter().map(lookup).collect(),
        to: to.iter().map(lookup).collect(),
        edge_types: edge_types.iter().map(String::as_str).collect(),
        weights: edge_weights.to_vec(),
    }
}

///////////////////////////////
// Structure transformations //
///////////////////////////////

/// Build a CSR adjacency from an edge list via counting sort.
///
/// Duplicate `(row, col)` pairs are kept as separate entries, in input order.
///
/// ### Params
///
/// * `n` - Number of rows (nodes)
/// * `rows` - Row index per edge
/// * `cols` - Column index per edge
/// * `vals` - Value per edge
///
/// ### Returns
///
/// Tuple of `(indptr, indices, values)`.
pub(crate) fn edges_to_csr<T>(
    n: usize,
    rows: &[u32],
    cols: &[u32],
    vals: &[T],
) -> (Vec<usize>, Vec<u32>, Vec<T>)
where
    T: BixverseFloat,
{
    let mut indptr = vec![0usize; n + 1];
    for &r in rows {
        indptr[r as usize + 1] += 1;
    }
    for i in 0..n {
        indptr[i + 1] += indptr[i];
    }

    let mut next = indptr[..n].to_vec();
    let mut indices = vec![0u32; rows.len()];
    let mut values = vec![T::zero(); rows.len()];
    for ((&r, &c), &v) in rows.iter().zip(cols).zip(vals) {
        let pos = next[r as usize];
        indices[pos] = c;
        values[pos] = v;
        next[r as usize] += 1;
    }

    (indptr, indices, values)
}

/// Generate a KNN graph adjacency matrix from a similarity matrix
///
/// ### Params
///
/// * `similarities` - The symmetric similarity matrix.
/// * `k` - Number of neighbours to take
///
/// ### Returns
///
/// The KNN adjacency matrix
pub fn get_knn_graph_adj<T>(similarities: &MatRef<T>, k: usize) -> Mat<T>
where
    T: BixverseFloat,
{
    assert_symmetric_mat!(similarities);
    let n = similarities.nrows();
    let mut adjacency: Mat<T> = Mat::zeros(n, n);

    // Row blocks, walking each column once per block so the reads are
    // contiguous in the column-major matrix.
    let blocks: Vec<usize> = (0..n).step_by(KNN_ROW_BLOCK).collect();
    let rows: Vec<Vec<(usize, T)>> = blocks
        .par_iter()
        .flat_map_iter(|&start| {
            let end = (start + KNN_ROW_BLOCK).min(n);
            let mut heaps: Vec<BinaryHeap<(RevOrderedFloat<T>, usize)>> = (start..end)
                .map(|_| BinaryHeap::with_capacity(k + 1))
                .collect();

            for j in 0..n {
                let col = similarities.col(j);
                for (heap, i) in heaps.iter_mut().zip(start..end) {
                    if i == j {
                        continue;
                    }
                    let entry = (RevOrderedFloat(col[i]), j);
                    // The entry that would be pushed and immediately popped
                    if heap.len() == k && heap.peek().is_some_and(|top| entry >= *top) {
                        continue;
                    }
                    heap.push(entry);
                    if heap.len() > k {
                        heap.pop();
                    }
                }
            }

            heaps.into_iter().map(|heap| {
                heap.into_iter()
                    .map(|(ordered_sim, index)| (index, ordered_sim.0))
                    .collect::<Vec<_>>()
            })
        })
        .collect();

    // Fill adjacency matrix
    for (i, neighbors) in rows.iter().enumerate() {
        for &(j, sim) in neighbors {
            adjacency[(i, j)] = sim;
        }
    }

    // Symmetrise tile by tile to avoid strided walks
    let two = T::from_f64(2.0).unwrap();
    for ib in (0..n).step_by(KNN_SYM_TILE) {
        for jb in (ib..n).step_by(KNN_SYM_TILE) {
            for j in jb..(jb + KNN_SYM_TILE).min(n) {
                for i in ib..(ib + KNN_SYM_TILE).min(n).min(j) {
                    let val = (adjacency[(i, j)] + adjacency[(j, i)]) / two;
                    adjacency[(i, j)] = val;
                    adjacency[(j, i)] = val;
                }
            }
        }
    }

    adjacency
}

/// Generate a Laplacian matrix from an adjacency matrix
///
/// ### Params
///
/// * `adjacency` - The symmetric adjacency matrix.
///
/// ### Returns
///
/// The Laplacian matrix
pub fn adjacency_to_laplacian<T>(adjacency: &MatRef<T>, normalise: bool) -> Mat<T>
where
    T: BixverseFloat,
{
    assert_symmetric_mat!(adjacency);
    let n = adjacency.nrows();

    // Column-wise accumulation, in the same order as a row sum
    let mut degrees = vec![T::zero(); n];
    for j in 0..n {
        for (d, &x) in degrees.iter_mut().zip(adjacency.col(j).iter()) {
            *d += x;
        }
    }

    if !normalise {
        let mut laplacian = adjacency.cloned();
        for j in 0..n {
            for i in 0..n {
                if i != j {
                    laplacian[(i, j)] = -adjacency[(i, j)];
                }
            }
            laplacian[(j, j)] = degrees[j] - adjacency[(j, j)];
        }
        laplacian
    } else {
        let inv_sqrt_d: Vec<T> = degrees
            .iter()
            .map(|&d| {
                if d > T::from_f64(1e-10).unwrap() {
                    T::one() / d.sqrt()
                } else {
                    T::zero()
                }
            })
            .collect();
        let mut laplacian = Mat::zeros(n, n);
        for j in 0..n {
            laplacian[(j, j)] = T::one();
            for i in 0..n {
                laplacian[(i, j)] -= inv_sqrt_d[i] * adjacency[(i, j)] * inv_sqrt_d[j];
            }
        }
        laplacian
    }
}

/// Build an undirected SparseGraph from a flat edge-pair list.
///
/// Counting-sort CSR build, then a parallel per-row sort of the column
/// indices. Duplicate edges are kept as separate entries (algorithms that scan
/// rows sum them), and a self-loop lands twice in its row, as in igraph.
///
/// ### Params
///
/// * `edges` - Flat edge list, pairs `[u, v, u, v, ...]`, 0-based
/// * `weights` - Edge weights, one per pair
/// * `n_nodes` - Number of nodes in the graph
///
/// ### Returns
///
/// Undirected graph with a symmetric, row-sorted CSR
pub fn edge_pairs_to_sparse_graph<T>(
    edges: &[usize],
    weights: &[T],
    n_nodes: usize,
) -> SparseGraph<T>
where
    T: BixverseFloat + std::iter::Sum + Send + Sync,
{
    let n_edges = weights.len();

    let mut indptr = vec![0usize; n_nodes + 1];
    for e in 0..n_edges {
        indptr[edges[2 * e] + 1] += 1;
        indptr[edges[2 * e + 1] + 1] += 1;
    }
    for i in 1..=n_nodes {
        indptr[i] += indptr[i - 1];
    }

    let nnz = indptr[n_nodes];
    let mut indices = vec![0usize; nnz];
    let mut data = vec![T::zero(); nnz];
    let mut cursor = indptr.clone();

    for e in 0..n_edges {
        let i = edges[2 * e];
        let j = edges[2 * e + 1];
        let w = weights[e];

        indices[cursor[i]] = j;
        data[cursor[i]] = w;
        cursor[i] += 1;

        indices[cursor[j]] = i;
        data[cursor[j]] = w;
        cursor[j] += 1;
    }

    // sort column indices within each row, keeping data aligned
    let mut rows: Vec<(&mut [usize], &mut [T])> = Vec::with_capacity(n_nodes);
    let (mut idx_rest, mut data_rest) = (indices.as_mut_slice(), data.as_mut_slice());
    for node in 0..n_nodes {
        let len = indptr[node + 1] - indptr[node];
        let (idx_row, idx_tail) = idx_rest.split_at_mut(len);
        let (data_row, data_tail) = data_rest.split_at_mut(len);
        rows.push((idx_row, data_row));
        idx_rest = idx_tail;
        data_rest = data_tail;
    }

    rows.par_iter_mut()
        .for_each_init(Vec::<(usize, T)>::new, |scratch, (idx_row, data_row)| {
            if idx_row.len() < 2 {
                return;
            }
            scratch.clear();
            scratch.extend(idx_row.iter().copied().zip(data_row.iter().copied()));
            scratch.sort_unstable_by_key(|&(node, _)| node);
            for ((i, d), &(node, w)) in idx_row.iter_mut().zip(data_row.iter_mut()).zip(&*scratch) {
                *i = node;
                *d = w;
            }
        });

    let adjacency = CompressedSparseData2 {
        data,
        indices: indices.index_cast(),
        indptr: indptr.index_cast(),
        cs_type: CompressedSparseFormat::Csr,
        data_2: None,
        shape: (n_nodes, n_nodes),
    };

    SparseGraph::new(n_nodes, adjacency, false)
}

///////////
// Tests //
///////////

#[cfg(test)]
mod tests {
    use super::*;
    use bixverse_core::core::math::sparse::CompressedSparseData2;
    use faer::Mat;

    /// Degrees, total weight and neighbour slices read straight off the CSR.
    #[test]
    fn test_sparse_graph_properties() {
        // Simple line graph: 0 <-> 1 <-> 2
        let data = vec![1.0, 1.0, 1.0, 1.0];
        let indices = vec![1, 0, 2, 1]; // neighbors
        let indptr = vec![0, 1, 3, 4]; // node offsets
        let csr =
            CompressedSparseData2::<f64, f64>::new_csr(&data, &indices, &indptr, None, (3, 3));

        let graph = SparseGraph::new(3, csr, false);

        assert_eq!(graph.get_node_degree(0), 1);
        assert_eq!(graph.get_node_degree(1), 2);
        assert_eq!(graph.total_weight(), 2.0); // 2 undirected edges of weight 1.0

        let (n0_neighbors, n0_weights) = graph.get_neighbours(0);
        assert_eq!(n0_neighbors, &[1]);
        assert_eq!(n0_weights, &[1.0]);
    }

    /// The unnormalised Laplacian is `D - A`, degrees on the diagonal.
    #[test]
    fn test_adjacency_to_laplacian() {
        // Adjacency for 0 <-> 1
        let mut adj: Mat<f64> = Mat::zeros(2, 2);
        adj[(0, 1)] = 1.0;
        adj[(1, 0)] = 1.0;

        // Unnormalized Laplacian: L = D - A
        // D is [[1, 0], [0, 1]]
        let lap = adjacency_to_laplacian(&adj.as_ref(), false);

        assert!((lap[(0, 0)] - 1.0).abs() < 1e-6);
        assert!((lap[(1, 1)] - 1.0).abs() < 1e-6);
        assert!((lap[(0, 1)] - (-1.0)).abs() < 1e-6);
        assert!((lap[(1, 0)] - (-1.0)).abs() < 1e-6);
    }

    /// String node names map onto indices without dropping or duplicating edges.
    #[test]
    fn test_graph_from_strings_with_attributes() {
        let nodes = vec!["A".to_string(), "B".to_string(), "C".to_string()];
        let node_types = vec!["x".to_string(), "y".to_string(), "x".to_string()];
        let from = vec!["A".to_string(), "B".to_string(), "C".to_string()];
        let to = vec!["B".to_string(), "C".to_string(), "B".to_string()];
        let edge_types = vec!["e".to_string(), "f".to_string(), "e".to_string()];
        let weights = vec![1.5, 2.5, 0.5];

        let graph = graph_from_strings_with_attributes(
            &nodes,
            &node_types,
            &from,
            &to,
            &edge_types,
            &weights,
        );

        assert_eq!(graph.node_types, vec!["x", "y", "x"]);
        assert_eq!(graph.from, vec![0, 1, 2]);
        assert_eq!(graph.to, vec![1, 2, 1]);
        assert_eq!(graph.edge_types, vec!["e", "f", "e"]);
        assert_eq!(graph.weights, weights);
    }
}