Eigenfactor

TL;DR

The Eigenfactor is a journal impact metric that uses citation network structure and Eigenvector centrality to assess journal influence, addressing limitations of simple citation counts.

Contribution

This paper introduces the Eigenfactor metric, leveraging network analysis and Eigenvector centrality to evaluate journal importance more accurately.

Findings

Eigenfactor correlates with expert assessments

Addresses limitations of citation counts

Proposed as an alternative metric

Abstract

The Eigenfactor is a journal metric, which was developed by Bergstrom and his colleagues at the University of Washington. They invented the Eigenfactor as a response to the criticism against the use of simple citation counts. The Eigenfactor makes use of the network structure of citations, i.e. citations between journals, and establishes the importance, influence or impact of a journal based on its location in a network of journals. The importance is defined based on the number of citations between journals. As such, the Eigenfactor algorithm is based on Eigenvector centrality. While journal-based metrics have been criticized, the Eigenfactor has also been suggested as an alternative in the widely used San Francisco Declaration on Research Assessment (DORA).