Abstract
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 an algorithm which simultaneously computes reputation of users and fitness of papers in a bipartite network representing 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 input data is extended to a multilayer network including users, papers and authors and the algorithm is correspondingly modified, the resulting 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. We finally show that our algorithm is robust against persistent authors (spammers) which makes the method readily applicable to the existing online scientific communities.
Highlights
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]
Scores obtained with bipartite HITS (biHITS) correlate least with user ability and item fitness and are at the same time biased towards old items and, even more, active users
We begin our analysis by inspecting algorithms without author credit: popularity ranking (POP), where popularity is measured by the number of downloads, and bipartite HITS
Summary
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]. Complex networks [10] have provided a fruitful ground for research of reputation systems with PageRank [11, 12] and HITS [13] being the classical examples. Building on BiHITS, a bipartite version of HITS [15], [16] presents a so-called QTR algorithm which has been developed for online communities. This algorithm co-determines item quality (which we refer to as fitness ) and user reputation from a multilayer network which consists of a bipartite user-item network and a monopartite social network
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