C# Class TemporalNetworks.BetweennessPref

This class provides static methods to compute both betweenness preference matrices as well as betweenness preference of nodes in temporal networks

Afficher le fichier Open project: IngoScholtes/TemporalNetworks Class Usage Examples

Méthodes publiques

Méthode	Description
GetBetweennessPref ( TemporalNetwork temp_net, string x, bool normalized = false ) : double	Computes the (scalar) betwenness preference of a node
GetBetweennessPref ( WeightedNetwork aggregate_net, string x, double P, bool normalized = false ) : double	Computes the scalar betwenness preference of a node based on its normalized betweenness preference matrix
GetBetweennessPrefDist ( TemporalNetwork temp_net ) : IEnumerable	Parallely computes the betweenness preference distribution of all nodes in a temporal network
GetBetweennessPrefMatrix ( TemporalNetwork temp_net, string x, int>.Dictionary &index_pred, int>.Dictionary &index_succ, bool normalized = true ) : ].double[	Computes the betweenness preference matrix of a node based on the set of two-paths of a node. By this we essentially implement equations (1) and (2). If additionally the normalization parameter is set, equation (3) will be computed.
GetUncorrelatedBetweennessPrefMatrix ( TemporalNetwork temp_net, string x, int>.Dictionary &index_pred, int>.Dictionary &index_succ ) : ].double[	Computes the baseline betweenness preference matrix of a node under the assumption that the temporal network does not contain a betweenness preference correlation. This corresponds to equation (5) in the paper.
GetUncorrelatedBetweennessPrefMatrix ( WeightedNetwork aggregate_net, string v, int>.Dictionary &index_pred, int>.Dictionary &index_succ ) : ].double[	Computes the baseline betweenness preference matrix of a node under the assumption that the temporal network does not contain a betweenness preference correlation. This corresponds to equation (5) in the paper.
NormalizeMatrix ( string x, WeightedNetwork aggregate_net, double B ) : ].double[	Computes a normalized version P of a given betweenness preference matrix B.

Private Methods

Méthode	Description
Entropy ( double marginal_s ) : double

Method Details

GetBetweennessPref() public static méthode

Computes the (scalar) betwenness preference of a node

public static GetBetweennessPref ( TemporalNetwork temp_net, string x, bool normalized = false ) : double
temp_net	TemporalNetwork	The temporal network for which to compute betweenness preference
x	string	The node for which to compute betweenness preference
normalized	bool
Résultat	double

GetBetweennessPref() public static méthode

Computes the scalar betwenness preference of a node based on its normalized betweenness preference matrix

public static GetBetweennessPref ( WeightedNetwork aggregate_net, string x, double P, bool normalized = false ) : double
aggregate_net	WeightedNetwork	The temporal network for which to compute betweenness preference
x	string	The node for which to compute betweenness preference
P	double	The betweenness preference matrix based on which betw. pref. will be computed
normalized	bool
Résultat	double

GetBetweennessPrefDist() public static méthode

Parallely computes the betweenness preference distribution of all nodes in a temporal network

public static GetBetweennessPrefDist ( TemporalNetwork temp_net ) : IEnumerable
temp_net	TemporalNetwork	The temporal network to analyze
Résultat	IEnumerable

GetBetweennessPrefMatrix() public static méthode

Computes the betweenness preference matrix of a node based on the set of two-paths of a node. By this we essentially implement equations (1) and (2). If additionally the normalization parameter is set, equation (3) will be computed.

public static GetBetweennessPrefMatrix ( TemporalNetwork temp_net, string x, int>.Dictionary &index_pred, int>.Dictionary &index_succ, bool normalized = true ) : ].double[
temp_net	TemporalNetwork	The temporal network to compute betweeness preference for
x	string	The node for which to compute the betweenness preference matrix
index_pred	int>.Dictionary	A mapping of nodes to columns in the betweenness preference matrix
index_succ	int>.Dictionary	A mapping of nodes to rows in the betweenness preference matrix
normalized	bool
Résultat	].double[

GetUncorrelatedBetweennessPrefMatrix() public static méthode

Computes the baseline betweenness preference matrix of a node under the assumption that the temporal network does not contain a betweenness preference correlation. This corresponds to equation (5) in the paper.

public static GetUncorrelatedBetweennessPrefMatrix ( TemporalNetwork temp_net, string x, int>.Dictionary &index_pred, int>.Dictionary &index_succ ) : ].double[
temp_net	TemporalNetwork	The temporal network for which to compute the matrix
x	string	The node to compute the baseline betweenness preference for
index_pred	int>.Dictionary	Indices of predecessor nodes in the betweenness preference matrix
index_succ	int>.Dictionary	Indices of successor nodes in the betweenness preference matric
Résultat	].double[

GetUncorrelatedBetweennessPrefMatrix() public static méthode

public static GetUncorrelatedBetweennessPrefMatrix ( WeightedNetwork aggregate_net, string v, int>.Dictionary &index_pred, int>.Dictionary &index_succ ) : ].double[
aggregate_net	WeightedNetwork	The weighted, aggregate ego network of node x based on which the matrix will be computed
v	string	The node to compute the baseline betweenness preference for
index_pred	int>.Dictionary	Indices of predecessor nodes in the betweenness preference matrix
index_succ	int>.Dictionary	Indices of successor nodes in the betweenness preference matric
Résultat	].double[

NormalizeMatrix() public static méthode

Computes a normalized version P of a given betweenness preference matrix B.

public static NormalizeMatrix ( string x, WeightedNetwork aggregate_net, double B ) : ].double[
x	string	The node for which the normalized matrix is computed
aggregate_net	WeightedNetwork	The weighted aggregate network
B	double	The betweenness preference matrix that shall be normalized
Résultat	].double[