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The Eigenfactor Metrics provide an alternative way of evaluating scholarly journals based on an iterative ranking procedure analogous to Google's PageRank algorithm. These metrics have recently been adopted by Thomson-Reuters and are listed alongside the Impact Factor in the Journal Citation Reports. But do these metrics differ sufficiently so as to be a(More)
The bibliometric measure impact factor is a leading indicator of journal influence, and impact factors are routinely used in making decisions ranging from selecting journal subscriptions to allocating research funding to deciding tenure cases. Yet journal impact factors have increased gradually over time, and moreover impact factors vary widely across(More)
Limited time and budgets have created a legitimate need for quantitative measures of scholarly work. The well-known journal impact factor is the leading measure of this sort; here we describe an alternative approach based on the full structure of the scholarly citation network. The Eigen-factor Metrics—Eigenfactor Score and Article Influence Score—use an(More)
Random walks on networks is the standard tool for modelling spreading processes in social and biological systems. This first-order Markov approach is used in conventional community detection, ranking and spreading analysis, although it ignores a potentially important feature of the dynamics: where flow moves to may depend on where it comes from. Here we(More)
Gender disparities appear to be decreasing in academia according to a number of metrics, such as grant funding, hiring, acceptance at scholarly journals, and productivity, and it might be tempting to think that gender inequity will soon be a problem of the past. However, a large-scale analysis based on over eight million papers across the natural sciences,(More)
In this article, we show how the Eigenfactor score, originally designed for ranking scholarly journals, can be adapted to rank the scholarly output of authors, institutions , and countries based on author-level citation data. Using the methods described in this article, we provide Eigenfactor rankings for 84,808 disambiguated authors of 240,804 papers in(More)
Capturing dynamics of the spread of information and disease with random flow on networks is a paradigm. We show that this conventional approach ignores an important feature of the dynamics: where flow moves to depends on where it comes from. That is, memory matters. We analyze multi-step pathways from different systems and show that ignoring memory(More)
We describe the experimental recommendation platform created in collaboration with the Social Science Research Network (SSRN). This system allows for researchers to test recommendation algorithm on SSRN's users and quickly collect feedback on the efficacy of their recommendations. We further describe a test run performed using EigenFactor recommends and(More)