Struct Node2Vec 

pub struct Node2Vec<S = Untrained> { /* private fields */ }

Node2Vec Algorithm

Node2vec is a framework for learning continuous feature representations for nodes in networks. It uses biased random walks to generate contexts that preserve both local and global network structure.

Implementations

impl Node2Vec<Untrained>

pub fn new() -> Self

Create a new Node2Vec instance

pub fn n_components(self, n_components: usize) -> Self

Set the number of components

pub fn walk_length(self, walk_length: usize) -> Self

Set the walk length

pub fn num_walks(self, num_walks: usize) -> Self

Set the number of walks

pub fn p(self, p: f64) -> Self

Set the return parameter (p)

pub fn q(self, q: f64) -> Self

Set the in-out parameter (q)

pub fn window_size(self, window_size: usize) -> Self

Set the window size

pub fn min_count(self, min_count: usize) -> Self

Set the minimum count

pub fn batch_words(self, batch_words: usize) -> Self

Set the batch words

pub fn epochs(self, epochs: usize) -> Self

Set the number of epochs

pub fn learning_rate(self, learning_rate: f64) -> Self

Set the learning rate

pub fn negative_samples(self, negative_samples: usize) -> Self

Set the number of negative samples

pub fn random_state(self, random_state: Option<u64>) -> Self

Set the random state

impl Node2Vec<Node2VecTrained>

pub fn node_embeddings(&self) -> &Array2<f64>

Get the learned node embeddings

pub fn vocab(&self) -> &HashMap<usize, usize>

Get the vocabulary mapping

Trait Implementations

impl<S: Clone> Clone for Node2Vec<S>

fn clone(&self) -> Node2Vec<S>

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<S: Debug> Debug for Node2Vec<S>

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

Formats the value using the given formatter.

impl Default for Node2Vec<Untrained>

fn default() -> Self

Returns the “default value” for a type.

impl Estimator for Node2Vec<Untrained>

type Config = ()

Configuration type for the estimator

type Error = SklearsError

Error type for the estimator

type Float = f64

The numeric type used by this estimator

fn config(&self) -> &Self::Config

Get estimator configuration

fn validate_config(&self) -> Result<(), SklearsError>

Validate estimator configuration with detailed error context

fn check_compatibility( &self, n_samples: usize, n_features: usize, ) -> Result<(), SklearsError>

Check if estimator is compatible with given data dimensions

fn metadata(&self) -> EstimatorMetadata

Get estimator metadata

impl Estimator for Node2Vec<Node2VecTrained>

type Config = ()

Configuration type for the estimator

type Error = SklearsError

Error type for the estimator

type Float = f64

The numeric type used by this estimator

fn config(&self) -> &Self::Config

Get estimator configuration

fn validate_config(&self) -> Result<(), SklearsError>

Validate estimator configuration with detailed error context

fn check_compatibility( &self, n_samples: usize, n_features: usize, ) -> Result<(), SklearsError>

Check if estimator is compatible with given data dimensions

fn metadata(&self) -> EstimatorMetadata

Get estimator metadata

impl Fit<ArrayBase<ViewRepr<&f64>, Dim<[usize; 2]>>, ()> for Node2Vec<Untrained>

type Fitted = Node2Vec<Node2VecTrained>

The fitted model type

fn fit(self, x: &ArrayView2<'_, Float>, _y: &()) -> SklResult<Self::Fitted>

Fit the model to the provided data with validation

fn fit_with_validation( self, x: &X, y: &Y, _x_val: Option<&X>, _y_val: Option<&Y>, ) -> Result<(Self::Fitted, FitMetrics), SklearsError>

where Self: Sized,

Fit with custom validation and early stopping

impl Transform<ArrayBase<ViewRepr<&f64>, Dim<[usize; 2]>>, ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>>> for Node2Vec<Node2VecTrained>

fn transform(&self, x: &ArrayView2<'_, Float>) -> SklResult<Array2<Float>>

Transform the input data