Sabir Ribas

Sabir Ribas, Alberto Ueda, Rodrygo L. T. Santos et al.

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 citation-based 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.

Sabir Ribas, Berthier Ribeiro-Neto, Edmundo de Souza e Silva et al.

To manage the problem of having a higher demand for resources than availability of funds, research funding agencies usually rank the major research groups in their area of knowledge. This ranking relies on a careful analysis of the research groups in terms of their size, number of PhDs graduated, research results and their impact, among other variables. While research results are not the only variable to consider, they are frequently given special attention because of the notoriety they confer to the researchers and the programs they are affiliated with. In here we introduce a new metric for quantifying publication output, called R-Score for reputation-based score, which can be used in support to the ranking of research groups or programs. The novelty is that the metric depends solely on the listings of the publications of the members of a group, with no dependency on citation counts. R-Score has some interesting properties: (a) it does not require access to the contents of published material, (b) it can be curated to produce highly accurate results, and (c) it can be naturally used to compare publication output of research groups (e.g., graduate programs) inside a same country, geographical area, or across the world. An experiment comparing the publication output of 25 CS graduate programs from Brazil suggests that R-Score can be quite useful for providing early insights into the publication patterns of the various research groups one wants to compare.