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This study introduces an algorithm to rank scientific papers and users in online communities. The method effectively identifies top research and researchers, even with spam, improving information filtering for scientists.

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Area of Science:

  • Bibliometrics
  • Network Science
  • Computational Social Science

Background:

  • Increasing scientific output complicates tracking research progress.
  • Online scientific communities require effective information filtering tools.

Purpose of the Study:

  • Develop an algorithm to simultaneously assess user reputation and paper quality in online scientific networks.
  • Enhance information filtering within scientific communities.

Main Methods:

  • Proposed a novel algorithm for bipartite networks to compute user reputation and paper fitness.
  • Evaluated algorithm variants using simulated and real-world data (Econophysics Forum).
  • Extended the algorithm to multilayer networks (users, papers, authors) for improved performance.

Main Results:

  • The multilayer network approach significantly improved performance.
  • Identified top papers with higher citation counts and top authors with higher h-indexes compared to other methods.
  • Demonstrated algorithm robustness against persistent authors (spammers).

Conclusions:

  • The developed algorithm effectively filters information in online scientific communities.
  • The multilayer network extension offers superior performance in identifying influential papers and authors.
  • The method is robust and applicable to existing online scientific platforms.