Neng-Pin Lu
Biographic Data
| ID | 10329445 |
|---|---|
| NAME | Neng-Pin Lu |
| GIVEN NAMES | Neng-Pin |
| FAMILY NAME | Lu |
| SIGNATURE | LU N |
| AFFILIATIONS | Chang Gung University |
| VERIFIED | No |
| TOTAL WORKS | 4 |
| TOTAL CITATIONS | 1 |
| AUTHOR COUNT | 4 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2017 |
| LATEST PUBLICATION YEAR | 2021 |
| H-INDEX | 1 |
A measure of centrality based on a reciprocally perturbed Markov chainfor asymmetric relations
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…
A hierarchical walk-based measure of centrality based on reachability between strongly connected components in a digraph
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 bu…
Using principal eigenvectors of adjacency matrices with added diagonal weights to compose centrality measures and identify bowtie structures for a digraph
Principal eigenvectors of adjacency matrices are often adopted as measures of centrality for a graph or digraph. However, previous principal-eigenvector-like measures for a digraph usually consider only the strongly connected component whose adjacency submatrix has the largest eigenvalue. In this paper, for each and every strongly connected component in a digraph, we add weights to diagonal elements of its member nodes in the adjacency matrix suc…
Aggregating nonnegative eigenvectors of the adjacency matrix as a measure of centrality for a directed graph
Eigenvector centrality is a popular measure that uses the principal eigenvector of the adjacency matrix to distinguish importance of nodes in a graph. To find the principal eigenvector, the power method iterating from a random initial vector is often adopted. In this article, we consider the adjacency matrix of a directed graph and choose suitable initial vectors according to strongly connected components of the graph instead so that nonnegative …
Using principal eigenvectors of adjacency matrices with added diagonal weights to compose centrality measures and identify bowtie structures for a digraph
Principal eigenvectors of adjacency matrices are often adopted as measures of centrality for a graph or digraph. However, previous principal-eigenvector-like measures for a digraph usually consider only the strongly connected component whose adjacency submatrix has the largest eigenvalue. In this paper, for each and every strongly connected component in a digraph, we add weights to diagonal elements of its member nodes in the adjacency matrix suc…
Aggregating nonnegative eigenvectors of the adjacency matrix as a measure of centrality for a directed graph
Eigenvector centrality is a popular measure that uses the principal eigenvector of the adjacency matrix to distinguish importance of nodes in a graph. To find the principal eigenvector, the power method iterating from a random initial vector is often adopted. In this article, we consider the adjacency matrix of a directed graph and choose suitable initial vectors according to strongly connected components of the graph instead so that nonnegative …
Using principal eigenvectors of adjacency matrices with added diagonal weights to compose centrality measures and identify bowtie structures for a digraph
Principal eigenvectors of adjacency matrices are often adopted as measures of centrality for a graph or digraph. However, previous principal-eigenvector-like measures for a digraph usually consider only the strongly connected component whose adjacency submatrix has the largest eigenvalue. In this paper, for each and every strongly connected component in a digraph, we add weights to diagonal elements of its member nodes in the adjacency matrix suc…
A hierarchical walk-based measure of centrality based on reachability between strongly connected components in a digraph
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 bu…
A measure of centrality based on a reciprocally perturbed Markov chainfor asymmetric relations
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…
Adjacency matrix (4 works) · Centrality (4 works) · Combinatorics (4 works) · Complex Network Analysis Techniques (4 works) · Computer Science (4 works) · Graph (4 works) · Graph theory and applications (4 works) · Mathematics (4 works) · Opinion Dynamics and Social Influence (4 works) · Data mining (3 works)