A measure of centrality based on a reciprocally perturbed Markov chainfor asymmetric relations

Neng-Pin Lu

The Journal of Mathematical Sociology, 2022, vol. 46, issue 3, 246-265

Abstract: In digraphs representing asymmetric relations, the measured scores of previous spectral rankings are usually dominated by nodes in the largest strongly connected component. In our previous work, we proposed hierarchical alpha centrality to give higher scores for more reachable nodes not in the largest strongly connected component. However, without careful consideration of damping parameters, the scores obtained by this method may be unbounded. In this paper, we normalize the adjacency matrix to be stochastic, subsequently damping the resulting Markov chain with a reciprocal perturbation at each and every non-zero transition, and propose a new hierarchical measure of centrality for asymmetric relations. The proposed measure simplifies damping and ensures that the measured scores are bounded.

Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/0022250X.2021.1885402 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:gmasxx:v:46:y:2022:i:3:p:246-265

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/gmas20

DOI: 10.1080/0022250X.2021.1885402

Access Statistics for this article

The Journal of Mathematical Sociology is currently edited by Noah Friedkin

More articles in The Journal of Mathematical Sociology from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().