Static Ranking of Scholarly Papers using Article-Level Eigenfactor (ALEF)

TL;DR

This paper introduces ALEF, a novel citation-based ranking algorithm for scholarly articles, evaluated through the 2016 WSDM Cup Challenge on the Microsoft Academic Graph, achieving high accuracy in static article ranking.

Contribution

The paper presents ALEF, a new citation-based ranking method, and demonstrates its effectiveness in large-scale scholarly data ranking competitions.

Findings

ALEF achieved a score of 0.676, placing second in the contest.

ALEF outperformed other algorithms that used multiple data facets.

The approach validates citation-based metrics for static scholarly article ranking.

Abstract

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.