Description Usage Arguments Details Value References See Also Examples
The hub scores of the vertices are defined as the principal eigenvector of A*t(A), where A is the adjacency matrix of the graph.
1 2 
graph 
The input graph. 
scale 
Logical scalar, whether to scale the result to have a maximum score of one. If no scaling is used then the result vector has unit length in the Euclidean norm. 
weights 
Optional positive weight vector for calculating weighted
scores. If the graph has a 
options 
A named list, to override some ARPACK options. See

For undirected matrices the adjacency matrix is symmetric and the hub
scores are the same as authority scores, see
authority_score
.
A named list with members:
vector 
The authority/hub scores of the vertices. 
value 
The corresponding eigenvalue of the calculated principal eigenvector. 
options 
Some information about the ARPACK computation, it has
the same members as the 
J. Kleinberg. Authoritative sources in a hyperlinked environment. Proc. 9th ACMSIAM Symposium on Discrete Algorithms, 1998. Extended version in Journal of the ACM 46(1999). Also appears as IBM Research Report RJ 10076, May 1997.
authority_score
,
eigen_centrality
for eigenvector centrality,
page_rank
for the Page Rank scores. arpack
for
the underlining machinery of the computation.
1 2 3 4 5 6 7 
Attaching package: 'igraph'
The following objects are masked from 'package:stats':
decompose, spectrum
The following object is masked from 'package:base':
union
[1] 3.330669e16 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
[6] 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
[1] 1 1 1 1 1 1 1 1 1 1
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.