Struct EigenvectorCentrality 

pub struct EigenvectorCentrality;

Eigenvector Centrality algorithm implementation.

Eigenvector centrality assigns importance scores to nodes based on connections to important nodes. The algorithm uses the power iteration method to compute the dominant eigenvector of the adjacency matrix.

Implementations

impl EigenvectorCentrality

pub fn compute<T: Transaction>( tx: &T, config: &EigenvectorCentralityConfig, ) -> GraphResult<EigenvectorCentralityResult>

Compute eigenvector centrality for all nodes in the graph.

Arguments

• tx - The transaction to use for graph access
• config - Configuration parameters for the algorithm

Returns

An EigenvectorCentralityResult containing scores for all nodes.

Algorithm

Uses the power iteration method:

1. Initialize all nodes with score 1/√n
2. Iteratively update scores: x(k+1) = A * x(k)
3. Normalize after each iteration
4. Check for convergence

pub fn compute_for_nodes<T: Transaction>( tx: &T, nodes: &[EntityId], config: &EigenvectorCentralityConfig, ) -> GraphResult<EigenvectorCentralityResult>

Compute eigenvector centrality for a subset of nodes.

Only considers edges within the specified subgraph.

Arguments

• tx - The transaction to use for graph access
• nodes - The nodes to include in the computation
• config - Configuration parameters for the algorithm