Ranking users, papers and authors in online scientific communities

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 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.

7 pages, 3 figures, 3 tables

References in corpus (2)

• Information filtering via Iterative Refinement
• Decoding Information from noisy, redundant, and intentionally-distorted sources

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• Ranking in evolving complex networks
• Computational Socioeconomics
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• A trust-based recommendation method using network diffusion processes
• Accumulative time-based ranking method to reputation evaluation in information networks
• Improving the performance of reputation evaluating by combining the structure of network and nonlinear recovery
• Predicting missing links via correlation between nodes
• Eliminating the effect of rating bias on reputation systems