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On graph combinatorics to improve eigenvector-based measures of centrality in directed networks

Author
Arratia, A.; Marijuan, C.
Type of activity
Journal article
Journal
Linear algebra and its applications
Date of publication
2016-09-01
Volume
504
First page
325
Last page
353
DOI
https://doi.org/10.1016/j.laa.2016.04.011 Open in new window
Project funding
Learning and Communication
Repository
http://hdl.handle.net/2117/90335 Open in new window
URL
http://www.sciencedirect.com/science/article/pii/S0024379516301100 Open in new window
Abstract
We present a combinatorial study on the rearrangement of links in the structure of directed networks for the purpose of improving the valuation of a vertex or group of vertices as established by an eigenvector-based centrality measure. We build our topological classification starting from unidirectional rooted trees and up to more complex hierarchical structures such as acyclic digraphs, bidirectional and cyclical rooted trees (obtained by closing cycles on unidirectional trees). We analyze diff...
Citation
Arratia, A., Marijuan, C. On graph combinatorics to improve eigenvector-based measures of centrality in directed networks. "Linear algebra and its applications", 1 Setembre 2016, vol. 504, p. 325-353.
Keywords
Centrality, Eigenvector, Network, PageRank, Topology
Group of research
LARCA - Laboratory of Relational Algorithmics, Complexity and Learnability

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