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A converging reputation ranking iteration method via the eigenvector.

Xiao-Lu Liu1, Chong Zhao2

  • 1School of Management Science and Engineering, Shandong University of Finance and Economics, Jinan, PR China.

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Summary
This summary is machine-generated.

This study introduces the EigenRank algorithm for ranking user reputation and object quality in online systems. EigenRank demonstrates superior accuracy and robustness against malicious ratings compared to existing methods.

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Area of Science:

  • Computer Science
  • Information Retrieval
  • Network Analysis

Background:

  • Online rating systems are crucial for reputation management.
  • Accurate ranking of user reputation and object quality is essential.

Purpose of the Study:

  • To introduce and validate the EigenRank algorithm for enhanced reputation and quality ranking.
  • To analyze the convergence properties and performance of EigenRank.

Main Methods:

  • Developed an iterative eigenvector-based algorithm (EigenRank).
  • Proved the convergence and analyzed the convergence speed of EigenRank.
  • Conducted experiments on synthetic and empirical networks.

Main Results:

  • EigenRank significantly outperformed IBeta and Vote Aggregation methods in AUC and Kendall's τ on synthetic data.
  • EigenRank showed superior accuracy and robustness against random and malicious rating attacks on empirical data.

Conclusions:

  • EigenRank offers an effective and robust solution for online user reputation identification.
  • The algorithm's performance is validated across diverse network types and attack scenarios.