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Network-Driven Reputation in Online Scientific Communities

Author

Listed:
  • Hao Liao
  • Rui Xiao
  • Giulio Cimini
  • Matúš Medo

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.

Suggested Citation

  • Hao Liao & Rui Xiao & Giulio Cimini & Matúš Medo, 2014. "Network-Driven Reputation in Online Scientific Communities," PLOS ONE, Public Library of Science, vol. 9(12), pages 1-18, December.
    • Handle: RePEc:plo:pone00:0112022
    DOI: 10.1371/journal.pone.0112022
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    References listed on IDEAS

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    3. Chen, P. & Xie, H. & Maslov, S. & Redner, S., 2007. "Finding scientific gems with Google’s PageRank algorithm," Journal of Informetrics, Elsevier, vol. 1(1), pages 8-15.
    4. Tao Zhou & Matúš Medo & Giulio Cimini & Zi-Ke Zhang & Yi-Cheng Zhang, 2011. "Emergence of Scale-Free Leadership Structure in Social Recommender Systems," PLOS ONE, Public Library of Science, vol. 6(7), pages 1-6, July.
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    Cited by:

    1. Leilei Wu & Zhuoming Ren & Xiao-Long Ren & Jianlin Zhang & Linyuan Lü, 2018. "Eliminating the Effect of Rating Bias on Reputation Systems," Complexity, Hindawi, vol. 2018, pages 1-11, February.
    2. B Ian Hutchins & Xin Yuan & James M Anderson & George M Santangelo, 2016. "Relative Citation Ratio (RCR): A New Metric That Uses Citation Rates to Measure Influence at the Article Level," PLOS Biology, Public Library of Science, vol. 14(9), pages 1-25, September.
    3. Ikuesan Richard Adeyemi & Shukor Abd Razak & Mazleena Salleh & Hein S Venter, 2016. "Observing Consistency in Online Communication Patterns for User Re-Identification," PLOS ONE, Public Library of Science, vol. 11(12), pages 1-27, December.
    4. Linhong Xu & Kun Ding & Yuan Lin & Chunbo Zhang, 2023. "Does citation polarity help evaluate the quality of academic papers?," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(7), pages 4065-4087, July.
    5. Guan-Nan Wang & Hui Gao & Lian Chen & Dennis N A Mensah & Yan Fu, 2015. "Predicting Positive and Negative Relationships in Large Social Networks," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-14, June.

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