The hub scores of the vertices are defined as the principal eigenvector of \(A A^T\), where \(A\) is the adjacency matrix of the graph.
hub_score(graph, scale = TRUE, weights = NULL, options = arpack_defaults)A named list with members:
The authority/hub scores of the vertices.
The corresponding eigenvalue of the calculated principal eigenvector.
Some information about the ARPACK computation, it has
the same members as the options member returned
by arpack, see that for documentation.
The input graph.
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.
Optional positive weight vector for calculating weighted
scores. If the graph has a weight edge attribute, then this is used
by default.
This function interprets edge weights as connection strengths. In the
random surfer model, an edge with a larger weight is more likely to be
selected by the surfer.
A named list, to override some ARPACK options. See
arpack for details.
For undirected matrices the adjacency matrix is symmetric and the hub
scores are the same as authority scores, see
authority_score.
J. Kleinberg. Authoritative sources in a hyperlinked environment. Proc. 9th ACM-SIAM 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.
## An in-star
g <- make_star(10)
hub_score(g)$vector
## A ring
g2 <- make_ring(10)
hub_score(g2)$vector
Run the code above in your browser using DataLab