Ranking users, papers and authors in online scientific communities
The ever-increasing quantity and complexity of scientific production have made it difficult for researchers to keep track of advances in their own fields. This, together with growing popularity of online scientific communities, calls for the development of effective information filtering tools. We propose here a method to simultaneously compute reputation of users and quality of scientific artifacts in an online scientific community. Evaluation on artificially-generated data and real data from the Econophysics Forum is used to determine the method’s best-performing variants. We show that when the method is extended by considering author credit, its performance improves on multiple levels. In particular, top papers have higher citation count and top authors have higher h-index than top papers and top authors chosen by other algorithms. Introduction. – Science is not a monolithic movement, but rather a complex enterprise divided in a multitude of fields and subfields, many of which enjoy rapidly increasing levels of activity [1, 2]. Even sub-disciplines have grown so broad that individual researchers cannot follow all possibly relevant developments. Despite swift growth of online scientific communities (such as ResearchGate, Mendeley, Academia.edu, VIVO, and SciLink) [3] which facilitate social contacts and exchange of information, finding relevant papers and authors still remains a daunting task, especially in lively research fields. More generally, reliance of the modern society on computer-mediated transactions has provoked extensive research of reputation systems which compute reputation scores for individual entities and thus reduce the information asymmetry between the involved parties [4, 5]. Perhaps more important than the immediately useful information is the proverbial shadow of the future—incentives for good behavior and penalties against offenses—generated by these systems [6, 7]. Reputation systems are now an organic part of most e-commerce web sites [8] and question & answer sites [9]. Complex networks [10] have provided a fruitful ground for research of reputation systems with PageRank [11,12] and HITS [13] being the classical examples. In [14], the authors extended HITS by introducing authority score of content providers and apply the resulting EigenRumor algorithm to rank blogs. Building on a bipartite version of HITS [15], [16] presents a QTR algorithm suited for online communities. This algorithm co-determines item quality and user reputation from a multilayer network which consists of a bipartite user-item network and a monopartite social network. We propose here a reputation algorithm designed especially for online scientific communities where researchers share relevant papers. We first simplify the QTR algorithm by neglecting the social network among users. This simplification reflects the fact that trust relationships are often not available and allows us to better study the algorithm’s output with respect to the remaining parameters. We then extend the algorithm by introducing author credit which is however computed differently than in the previously-mentioned EigenRumor. Since author credit is co-determined from the same data as paper quality and user reputation, this extension preserves an important advantage of QTR: Its reliance on implicit ratings which are easier to elicit than explicit ratings [8]. We use various standard metrics of research productivity (citation count, impact factor, and h-index) to demonstrate that the new algorithm outperforms other state-of-the-art algorithms. Algorithms. – An online community is assumed to consist of N users and M items (papers or other sort of scientific artifacts) which are labeled with Latin and Greek letters, respectively. The community is represented by a bipartite user-item network W where a weighted link between user i and item α exists if user i has interacted with