Simplified Relative Citation Ratio for Static Paper Ranking: UFMG/LATIN at WSDM Cup 2016

Abstract

Static rankings of papers play a key role in the academic search setting. Many features are commonly used in the literature to produce such rankings, some examples are citationbased metrics, distinct applications of PageRank, among others. More recently, learning to rank techniques have been successfully applied to combine sets of features producing effective results. In this work, we propose the metric S-RCR, which is a simplified version of a metric called Relative Citation Ratio -both based on the idea of a co-citation network. When compared to the classical version, our simplification S-RCR leads to improved efficiency with a reasonable effectiveness. We use S-RCR to rank over 120 million papers in the Microsoft Academic Graph dataset. By using this single feature, which has no parameters and does not need to be tuned, our team was able to reach the 3rd position in the first phase of the WSDM Cup 2016.

Citation analysis with microsoft academic

“…MAG has grown massively from 2015 to 2016 and, according to Wade, Kuasan, Yizhou, and Gulli (2016), it contains approximately 140 million publication records (83) 1 , 40 million authors (20), 3.5 million institutions (0.77), 60,000 journals (22,000), and 55,000 fields of study (50,000). Ribas, Ueda, Santos, Ribeiro-Neto, and Ziviani (2016) found that 59% of the papers in MAG are without citation information. Currently, MAG data can be accessed in three different ways: by using the MA search engine 2 , by downloading historical snapshots of MAG 3 , or by employing the AK API 4 .…”