constraint: Burt's constraint

In igraph: Network Analysis and Visualization

Burt's constraint

Description

Given a graph, constraint() calculates Burt's constraint for each vertex.

Usage

constraint(graph, nodes = V(graph), weights = NULL)

Arguments

graph	A graph object, the input graph.
nodes	The vertices for which the constraint will be calculated. Defaults to all vertices.
weights	The weights of the edges. If this is NULL and there is a weight edge attribute this is used. If there is no such edge attribute all edges will have the same weight.

Details

Burt's constraint is higher if ego has less, or mutually stronger related (i.e. more redundant) contacts. Burt's measure of constraint, C_i, of vertex i's ego network V_i, is defined for directed and valued graphs,

C_i=\sum_{j \in V_i \setminus \{i\}} (p_{ij}+\sum_{q \in V_i \setminus \{i,j\}} p_{iq} p_{qj})^2

for a graph of order (i.e. number of vertices) N, where proportional tie strengths are defined as

p_{ij} = \frac{a_{ij}+a_{ji}}{\sum_{k \in V_i \setminus \{i\}}(a_{ik}+a_{ki})},

a_{ij} are elements of A and the latter being the graph adjacency matrix. For isolated vertices, constraint is undefined.

Value

A numeric vector of constraint scores

Author(s)

Jeroen Bruggeman (https://sites.google.com/site/jebrug/jeroen-bruggeman-social-science) and Gabor Csardi [e-mail]

References

Burt, R.S. (2004). Structural holes and good ideas. American Journal of Sociology 110, 349-399.

See Also

Other structural.properties: bfs(), component_distribution(), connect(), coreness(), degree(), dfs(), distance_table(), edge_density(), feedback_arc_set(), girth(), is_dag(), is_matching(), knn(), laplacian_matrix(), reciprocity(), subcomponent(), subgraph(), topo_sort(), transitivity(), unfold_tree(), which_multiple(), which_mutual()

Examples

g <- sample_gnp(20, 5 / 20)
constraint(g)

igraph documentation built on Aug. 10, 2023, 9:08 a.m.