A note on a generalization of eigenvector centrality for bipartite graphs and applications

TL;DR

This paper introduces a generalized eigenvector centrality measure tailored for bipartite graphs, motivated by applications in rating systems for time-sensitive processes, expanding the traditional eigenvector centrality framework.

Contribution

It proposes a new eigenvector centrality generalization specifically designed for bipartite graphs, with theoretical foundations and potential applications in rating systems.

Findings

Defines a new centrality measure for bipartite graphs

Connects the measure to linear algebra principles

Suggests applications in rating systems for dynamic processes

Abstract

Eigenvector centrality is a linear algebra based graph invariant used in various rating systems such as webpage ratings for search engines. A generalization of the eigenvector centrality invariant is defined which is motivated by the need to design rating systems for bipartite graph models of time-sensitive and other processes. The linear algebra connection and some applications are described.