Struct AdjacencyMatrix 

pub struct AdjacencyMatrix<T: Float> {
    pub num_nodes: usize,
    pub adj: Array2<T>,
}

Dense adjacency matrix representation

Suitable for small, dense graphs. Memory usage: O(n²)

Fields

• num_nodes: Number of nodes in the graph
• adj: Dense adjacency matrix (num_nodes × num_nodes)

Example

let adj = AdjacencyMatrix::from_edges(5, &[(0,1), (1,2), (2,3)])?;

Fields

num_nodes: usize

adj: Array2<T>

Implementations

impl<T: Float> AdjacencyMatrix<T>

pub fn from_edges( num_nodes: usize, edges: &[(usize, usize)], ) -> GraphResult<Self>

Create adjacency matrix from edge list

Arguments

• num_nodes - Number of nodes
• edges - List of edges as (source, target) pairs

Returns

Adjacency matrix with 1.0 for edges, 0.0 otherwise

pub fn with_self_loops(&self) -> GraphResult<Self>

Create adjacency matrix with self-loops

pub fn degree_matrix(&self) -> GraphResult<Array1<T>>

Compute degree matrix

pub fn symmetric_normalize(&self) -> GraphResult<Array2<T>>

Normalize adjacency matrix: D^(-1/2) A D^(-1/2)

This is the symmetric normalization used in GCN

Trait Implementations

impl<T: Clone + Float> Clone for AdjacencyMatrix<T>

fn clone(&self) -> AdjacencyMatrix<T>

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl<T: Debug + Float> Debug for AdjacencyMatrix<T>

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.