Aggregating nonnegative eigenvectors of the adjacency matrix as a measure of centrality for a directed graph

Neng Pin Lu*

*此作品的通信作者

研究成果: 期刊稿件文章同行評審

4 引文 斯高帕斯(Scopus)

摘要

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 eigenvectors, including the principal one, can be found. Consequently, for aggregating nonnegative eigenvectors, we propose a weighted measure of centrality, called the aggregated-eigenvector centrality. Weighting each nonnegative eigenvector by the reachability of the associated strongly connected component, we can obtain a measure that follows a status hierarchy in a directed graph.

原文英語
頁(從 - 到)139-154
頁數16
期刊Journal of Mathematical Sociology
41
發行號3
DOIs
出版狀態已出版 - 2017
對外發佈

文獻附註

Publisher Copyright:
© 2017 Taylor & Francis.

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