Struct LeadingEigenvector 

pub struct LeadingEigenvector {
    pub membership: Vec<i64>,
    pub merges: Vec<(i64, i64)>,
    pub modularity: f64,
    pub eigenvalues: Vec<f64>,
    pub eigenvectors: Vec<Vec<f64>>,
    pub history: Vec<LeadingEigenvectorEvent>,
}

Result of Newman’s leading eigenvector method, see Graph::community_leading_eigenvector.

Fields

membership: Vec<i64>

Community id of each vertex after all the splits.

merges: Vec<(i64, i64)>

The splits, replayed backwards as merges of community ids (not vertex ids): with p final communities, the first pair forms community p, the second p + 1, … Use le_community_to_membership to undo splits.

modularity: f64

Modularity of the final division.

eigenvalues: Vec<f64>

Eigenvalue computed at each step (NaN for steps given by the initial partition); non-positive values did not result in a split.

eigenvectors: Vec<Vec<f64>>

Eigenvector computed at each step, restricted to the vertices of the community being split (empty for steps given by the initial partition).

history: Vec<LeadingEigenvectorEvent>

A trace of the algorithm.

Implementations

impl LeadingEigenvector

pub fn num_communities(&self) -> usize

Number of communities in membership.

pub fn sizes(&self) -> Vec<usize>

Size of each community, indexed by community id.

pub fn communities(&self) -> Vec<Vec<VertexId>>

The vertices of each community, indexed by community id.

Trait Implementations

impl Clone for LeadingEigenvector

fn clone(&self) -> LeadingEigenvector

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for LeadingEigenvector

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

Formats the value using the given formatter.

impl PartialEq for LeadingEigenvector

fn eq(&self, other: &LeadingEigenvector) -> bool

Tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl StructuralPartialEq for LeadingEigenvector