A hierarchical walk-based measure of centrality based on reachability between strongly connected components in a digraph
Bibliographic Data
| ID | 10142450 |
|---|---|
| Authors | Neng‐Pin Lu (0000-0002-4310-4171, Chang Gung University, corresponding author), Neng-Pin Lu (Chang Gung University) |
| Year | 2020 |
| Volume | 45 |
| Issue | 1 |
| Pages | 51-64 |
| Publication date | 2020-01-12 |
| Peer Reviewed | Yes |
| 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.2020.1711753 |
| OpenAlex | W2999228762 |
| Language | EN |
| Citations received | 1 |
| References cited | 23 |
For measuring the centrality in a digraph, Bonacich and Lloyd summarized a vector, from the power series of an attenuated adjacency matrix, as the alpha centrality. However, scores of alpha centrality are usually dominated by nodes in the strongly connected component, which owns the largest eigenvalue of the adjacency matrix. In this paper, based on reachability between strongly connected components, we consider not only the largest eigenvalue but also the other smaller ones to attenuate the adjacency matrix hierarchically; and obtain a measure from the power series of the hierarchically attenuated adjacency matrix. Consequently, we propose the hierarchical alpha centrality, which can yield higher scores for nodes at higher hierarchies of reachability in a digraph
Adjacency matrix · Betweenness centrality · Centrality · Combinatorics · Data mining · Digraph · Eigenvalues and eigenvectors · Graph · Matrix (chemical analysis · Measure (data warehouse · Physics · Reachability · Strongly connected component · Subspace topology · Complex Network Analysis Techniques · Computer Science · Graph theory and applications · Mathematics · Opinion Dynamics and Social Influence
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Understanding Social Networks
A New Status Index Derived from Sociometric Analysis
Connectivity in a citation network
A Graph-theoretic perspective on centrality
Contacts and influence
Eigenvector-like measures of centrality for asymmetric relations
Social Networks of Drug Users in High-Risk Sites
An Input-Output Approach to Clique Identification
Aggregating nonnegative eigenvectors of the adjacency matrix as a measure of centrality for a directed graph
Using principal eigenvectors of adjacency matrices with added diagonal weights to compose centrality measures and identify bowtie structures for a digraph
Factoring and weighting approaches to status scores and clique identification
Friendship networks and social status
Spectral ranking
Network Analysis
The Origins of Status Hierarchies
Theoretical Foundations for Centrality Measures
Power and Centrality
| Unique citing works | 1 |
|---|---|
| Citations per year | 0.2 |
| Citation span | 2021 - 2021 (1) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 1 |