# hub_score

0th

Percentile

##### Kleinberg's hub centrality scores.

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.

##### Usage
hub_score(graph, scale = TRUE, weights = NULL, options = arpack_defaults)
##### Arguments
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 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.

options

A named list, to override some ARPACK options. See arpack for details.

##### Details

For undirected matrices the adjacency matrix is symmetric and the hub scores are the same as authority scores, see authority_score.

##### Value

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 options member returned by arpack, see that for documentation.

##### References

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.

• hub_score
• hub.score
##### Examples
# NOT RUN {
## An in-star
g <- make_star(10)
hub_score(g)$vector ## A ring g2 <- make_ring(10) hub_score(g2)$vector
# }

Documentation reproduced from package igraph, version 1.2.2, License: GPL (>= 2)

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