package spectral

import "gonum.org/v1/gonum/graph/spectral"

Package spectral provides graph spectral analysis functions.

Index ¶

• type Laplacian
• func NewLaplacian(g graph.Undirected) Laplacian
• func NewRandomWalkLaplacian(g graph.Graph, damp float64) Laplacian
• func NewSymNormLaplacian(g graph.Undirected) Laplacian

Types ¶

type Laplacian ¶

type Laplacian struct {
	// Matrix holds the Laplacian matrix.
	mat.Matrix

	// Nodes holds the input graph nodes.
	Nodes []graph.Node

	// Index is a mapping from the graph
	// node IDs to row and column indices.
	Index map[int64]int
}

Laplacian is a graph Laplacian matrix.

func NewLaplacian ¶

func NewLaplacian(g graph.Undirected) Laplacian

NewLaplacian returns a Laplacian matrix for the simple undirected graph g. The Laplacian is defined as D-A where D is a diagonal matrix holding the degree of each node and A is the graph adjacency matrix of the input graph. If g contains self edges, NewLaplacian will panic.

func NewRandomWalkLaplacian ¶

func NewRandomWalkLaplacian(g graph.Graph, damp float64) Laplacian

NewRandomWalkLaplacian returns a damp-scaled random walk Laplacian matrix for the simple graph g. The random walk Laplacian is defined as I-D^(-1)A where D is a diagonal matrix holding the degree of each node and A is the graph adjacency matrix of the input graph. If g contains self edges, NewRandomWalkLaplacian will panic.

func NewSymNormLaplacian ¶

func NewSymNormLaplacian(g graph.Undirected) Laplacian

NewSymNormLaplacian returns a symmetric normalized Laplacian matrix for the simple undirected graph g. The normalized Laplacian is defined as I-D^(-1/2)AD^(-1/2) where D is a diagonal matrix holding the degree of each node and A is the graph adjacency matrix of the input graph. If g contains self edges, NewSymNormLaplacian will panic.

Source Files ¶

doc.go laplacian.go