Description Usage Arguments Details Value Author(s) References See Also Examples
This function calculates the eigenvector centrality for multiple graphs.
1 2 3 4 5 6 | eigen_centr_mult(
graphs,
normalize = c(0, 1, 2, 3, 4, 5),
col.names = NULL,
row.names = NULL
)
|
graphs |
a list of igraph objects. |
normalize |
how the normalization of eigenvector centrality should be treated. The options are as follows: |
col.names |
The names of each column (node labels). |
row.names |
The names of each row (subject). |
The eigenvector centrality is the eigenvector for each node corresponding to the largest eigenvalue of the matrix.
Eigenvector centrality differs from degree or strength. A node with many connections does not necessarily have a high eigenvector centrality. For example, a node may have many very weak connections that yield a large value for strength/degree. Likewise, a node with high eigenvector centrality may have few connections but be well connected to a small number of important nodes.
A vector of centrality scores for each node.
A matrix of the eigenvector centralities of each node for each subject.
Brandon Vaughan
Lohmann, G., Margulies, D. S., Horstmann, A., Pleger, B., Lepsien, J., Goldhahn, D., … Turner, R. (2010). Eigenvector Centrality Mapping for Analyzing Connectivity Patterns in fMRI Data of the Human Brain. PLoS ONE, 5(4), e10232. doi:10.1371/journal.pone.0010232
Fornito, A., Zalesky, A., & Bullmore, E. (2016). Centrality and Hubs. Chapter 5. Fundamentals of Brain Network Analysis, 137-161. doi:10.1016/b978-0-12-407908-3.00005-4
Rubinov, M. , Sporns, O. (2010) Complex network measures of brain connectivity: Uses and interpretations. NeuroImage 52:1059-69.
eigen_centr
leverage_centr_mult
1 | eigencent = eigen_centr_mult(graphs,row.names = subj_numbers,col.names = node_labels)
|
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