Static Ranking of Scholarly Papers using Article-Level Eigenfactor (ALEF)
This work addresses ranking scholarly papers for researchers and databases, but it is incremental as it applies a novel method to an existing benchmark.
The authors tackled the problem of static ranking of scholarly papers by testing the Article-Level Eigenfactor (ALEF) algorithm on a large dataset of 122M papers and 757M citations, achieving a score of 0.676 and second place in the WSDM Cup Challenge.
Microsoft Research hosted the 2016 WSDM Cup Challenge based on the Microsoft Academic Graph. The goal was to provide static rankings for the articles that make up the graph, with the rankings to be evaluated against those of human judges. While the Microsoft Academic Graph provided metadata about many aspects of each scholarly document, we focused more narrowly on citation data and used this contest as an opportunity to test the Article Level Eigenfactor (ALEF), a novel citation-based ranking algorithm, and evaluate its performance against competing algorithms that drew upon multiple facets of the data from a large, real world dataset (122M papers and 757M citations). Our final submission to this contest was scored at 0.676, earning second place.