A measure of centrality based on a reciprocally perturbed Markov chainfor asymmetric relations
Bibliographic Data
| ID | 10142319 |
|---|---|
| Authors | Neng‐Pin Lu (0000-0002-4310-4171, Chang Gung University, corresponding author), Neng-Pin Lu (Chang Gung University) |
| Year | 2021 |
| Volume | 46 |
| Issue | 3 |
| Pages | 246-265 |
| Publication date | 2021-02-27 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Journal of Mathematical Sociology (JOURNAL) |
| Journal identifiers | ISSN: 0022-250X • E-ISSN: 1545-5874 |
| Publisher | Taylor & Francis (PUBLISHER • GB) |
| DOI | 10.1080/0022250x.2021.1885402 |
| OpenAlex | W3134254287 |
| Language | EN |
| References cited | 32 |
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
Adjacency matrix · Bounded function · Centrality · Combinatorics · Data mining · Discrete mathematics · Graph · Markov chain · Mathematical analysis · Measure (data warehouse · Perturbation (astronomy · Physics · Reciprocal · Statistical physics · Statistics · Strongly connected component · Topology (electrical circuits · Complex Network Analysis Techniques · Computer Science · Graph theory and applications · Mathematics · Opinion Dynamics and Social Influence
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| Highly cited | No |
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