This vignette describes the use of centrality in signed networks.
Centrality indices for signed networks
There exist dozens of indices for networks with positive ties, but for signed networks they are rather scarce. The package implements three indices so far. Versions of degree and eigenvector centrality, and PN centrality by Everett & Borgatti.
Degree centrality can be calculated in four different ways with
degree_signed()
, specified by the type
parameter:

type="pos"
count only positive neighbors 
type="neg"
count only negative neighbors 
type="ratio"
positive neighbors/(positive neighbors+negative neighbors) 
type="net"
positive neighborsnegative neighbors
The mode
parameter can be used to get “in” and “out”
versions for directed networks.
The PN index is very similar to Katz status and Hubbell’s measure for networks with only positive ties. The technical details can be found in the paper by Everett & Borgatti.
The below example illustrates all indices with a network where signed degree can not distinguish vertices.
A < matrix(c(0, 1, 0, 1, 0, 0, 0, 1, 1, 0,
1, 0, 1, 1, 1, 1, 1, 0, 0, 0,
0, 1, 0, 1, 1, 0, 0, 0, 1, 0,
1, 1, 1, 0, 1, 1, 1, 0, 0, 0,
0, 1, 1, 1, 0, 1, 0, 1, 0, 1,
0, 1, 0, 1, 1, 0, 1, 0, 1, 1,
0, 1, 0, 1, 0, 1, 0, 1, 1, 1,
1, 0, 0, 0, 1, 0, 1, 0, 1, 0,
1, 0, 1, 0, 0, 1, 1, 1, 0, 1,
0, 0, 0, 0, 1, 1, 1, 0, 1, 0),10,10)
g < graph_from_adjacency_matrix(A,"undirected",weighted = "sign")
degree_signed(g,type="ratio")
#> [1] 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5
eigen_centrality_signed(g)
#> [1] 0.62214960 1.00000000 0.74518850 1.00000000 0.89990041 0.64289592
#> [7] 0.35828159 0.37471921 0.28087411 0.07834568
pn_index(g)
#> [1] 0.9009747 0.8613482 0.9076997 0.8613482 0.8410658 0.8496558 0.8617321
#> [8] 0.9015909 0.8509848 0.9072930
Note that PN centrality and eigenvector centrality differ significantly for this network.
cor(eigen_centrality_signed(g),pn_index(g),method = "kendall")
#> [1] 0.2
A note on eigenvector centrality
The adjacency matrix of a signed network may not have a dominant
eigenvalue. This means it is not clear which eigenvector should be used.
In addition it is possible for the adjacency matrix to have repeated
eigenvalues and hence multiple linearly independent eigenvectors. In
this case certain centralities can be arbitrarily assigned. The
eigen_centrality_signed()
function returns an error if this
is the case.
A < matrix(c( 0, 1, 1, 1, 0, 0, 1, 0, 0,
1, 0, 1, 0, 1, 0, 0, 1, 0,
1, 1, 0, 0, 0, 1, 0, 0, 1,
1, 0, 0, 0, 1, 1, 1, 0, 0,
0, 1, 0, 1, 0, 1, 0, 1, 0,
0, 0, 1, 1, 1, 0, 0, 0, 1,
1, 0, 0, 1, 0, 0, 0, 1, 1,
0, 1, 0, 0, 1, 0, 1, 0, 1,
0, 0, 1, 0, 0, 1, 1, 1, 0), 9, 9)
g < igraph::graph_from_adjacency_matrix(A,"undirected",weighted = "sign")
eigen_centrality_signed(g)
#> Error in eigen_centrality_signed(g): no dominant eigenvalue exists