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A converging reputation ranking iteration method via the eigenvector
1School of Management Science and Engineering, Shandong University of Finance and Economics, Jinan, PR China.
Plos One
|October 3, 2022
Summary
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.
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.
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