Given a graph,
constraint calculates Burt's constraint for each
constraint(graph, nodes = V(graph), weights = NULL)
A graph object, the input graph.
The vertices for which the constraint will be calculated. Defaults to all vertices.
The weights of the edges. If this is
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( [sum( p[i,j] + p[i,q] p[q,j], q in V[i], q != i,j )]^2, j in V[i], j != i).
for a graph of order (ie. number of vertices) N, where proportional tie strengths are defined as
p[i,j]=(a[i,j]+a[j,i]) / sum(a[i,k]+a[k,i], k in V[i], k != i),
a[i,j] are elements of A and the latter being the graph adjacency matrix. For isolated vertices, constraint is undefined.
A numeric vector of constraint scores
Jeroen Bruggeman (https://sites.google.com/site/jebrug/jeroen-bruggeman-social-science) and Gabor Csardi email@example.com
Burt, R.S. (2004). Structural holes and good ideas. American Journal of Sociology 110, 349-399.
g <- sample_gnp(20, 5/20) constraint(g)
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