network

Documentation

Overview

Package network provides network analysis functions.

Index

• func Betweenness(g graph.Graph) map[int]float64
• func BetweennessWeighted(g WeightedGraph, p path.AllShortest) map[int]float64
• func Closeness(g graph.Graph, p path.AllShortest) map[int]float64
• func Farness(g graph.Graph, p path.AllShortest) map[int]float64
• func HITS(g graph.Directed, tol float64) map[int]HubAuthority
• func Harmonic(g graph.Graph, p path.AllShortest) map[int]float64
• func PageRank(g graph.Directed, damp, tol float64) map[int]float64
• func PageRankSparse(g graph.Directed, damp, tol float64) map[int]float64
• func Residual(g graph.Graph, p path.AllShortest) map[int]float64
• type HubAuthority
• type WeightedGraph

Functions

func Betweenness

func Betweenness(g graph.Graph) map[int]float64

Betweenness returns the non-zero betweenness centrality for nodes in the unweighted graph g.

C_B(v) = \sum_{s ≠ v ≠ t ∈ V} (\sigma_{st}(v) / \sigma_{st})

where \sigma_{st} and \sigma_{st}(v) are the number of shortest paths from s to t, and the subset of those paths containing v respectively.

func BetweennessWeighted

func BetweennessWeighted(g WeightedGraph, p path.AllShortest) map[int]float64

BetweennessWeighted returns the non-zero betweenness centrality for nodes in the weighted graph g used to construct the given shortest paths.

C_B(v) = \sum_{s ≠ v ≠ t ∈ V} (\sigma_{st}(v) / \sigma_{st})

where \sigma_{st} and \sigma_{st}(v) are the number of shortest paths from s to t, and the subset of those paths containing v respectively.

func Closeness

func Closeness(g graph.Graph, p path.AllShortest) map[int]float64

Closeness returns the closeness centrality for nodes in the graph g used to construct the given shortest paths.

C(v) = 1 / \sum_u d(u,v)

For directed graphs the incoming paths are used. Infinite distances are not considered.

func Farness

func Farness(g graph.Graph, p path.AllShortest) map[int]float64

Farness returns the farness for nodes in the graph g used to construct the given shortest paths.

F(v) = \sum_u d(u,v)

For directed graphs the incoming paths are used. Infinite distances are not considered.

func HITS

func HITS(g graph.Directed, tol float64) map[int]HubAuthority

HITS returns the Hyperlink-Induced Topic Search hub-authority scores for nodes of the directed graph g. HITS terminates when the 2-norm of the vector difference between iterations is below tol. The returned map is keyed on the graph node IDs.

func Harmonic

func Harmonic(g graph.Graph, p path.AllShortest) map[int]float64

Harmonic returns the harmonic centrality for nodes in the graph g used to construct the given shortest paths.

H(v)= \sum_{u ≠ v} 1 / d(u,v)

For directed graphs the incoming paths are used. Infinite distances are not considered.

func PageRank

func PageRank(g graph.Directed, damp, tol float64) map[int]float64

PageRank returns the PageRank weights for nodes of the directed graph g using the given damping factor and terminating when the 2-norm of the vector difference between iterations is below tol. The returned map is keyed on the graph node IDs.

func PageRankSparse

func PageRankSparse(g graph.Directed, damp, tol float64) map[int]float64

PageRankSparse returns the PageRank weights for nodes of the sparse directed graph g using the given damping factor and terminating when the 2-norm of the vector difference between iterations is below tol. The returned map is keyed on the graph node IDs.

func Residual

func Residual(g graph.Graph, p path.AllShortest) map[int]float64

Residual returns the Dangalchev's residual closeness for nodes in the graph g used to construct the given shortest paths.

C(v)= \sum_{u ≠ v} 1 / 2^d(u,v)

For directed graphs the incoming paths are used. Infinite distances are not considered.

Types

type HubAuthority

type HubAuthority struct {
	Hub       float64
	Authority float64
}

HubAuthority is a Hyperlink-Induced Topic Search hub-authority score pair.

type WeightedGraph

type WeightedGraph interface {
	graph.Graph
	graph.Weighter
}

WeightedGraph is a graph with edge weights.