The Eigentrust algorithm for reputation management in P2P networks

By Sepandar D. Kamvar, Mario T. Schlosser, Hector Garcia-Molina. The Web Conference, 2003

4,025 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/288368f9ac2595ed3ab66d5d2a8eb23abd39d9f4

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Citations in the corpus

Free-riding and whitewashing in peer-to-peer systems (2004, background): “We .nd that when penalty is imposedonall newcomers inorder to avoidwhitewashing, system performance degrades signi.cantly only when the turnover rate among users is high.”
Cooperative peer groups in NICE (2003, background): “The Eigentrust [ 12 ] system focuses on taking pairwise trust values, i.e.”
Community Mining from Signed Social Networks (2007, background): “Kamvar et al. [8] studied a peer-to-peer file-sharing network, in which a peer assigns a trust value to those peers who have provided it with authentic files.”
The Second Eigenvalue of the Google Matrix (2003, methodology): “For example, in the field of peer-to-peer networks, the EigenTrust reputation algorithm given in [7] computes the principal eigenvector of a matrix of the form defined in equation 1.”
Iterative ranking from pair-wise comparisons (2012, background): “Notable examples of such network centralities include the random surfer model on the web graph for the version of the PageRank [14] which computes the relative importance of a web page, and a model of a random crawler in a peer-to-peer file-sharing network to assign trust value to each peer in Eigen”
Social ties and their relevance to churn in mobile telecom networks (2008, background): “reputation system called EigenTrust is presented in [14].”
Edge Weight Prediction in Weighted Signed Networks (2016, methodology): “.07) (0.32, -0.02) (0.34, -0.04) (0.94, 0.00) (0.87, -0.16) (0.55, 0.19) Signed Eigenvector Centrality (0.37, 0.17) (0.32, 0.23) (0.34, 0.11) (0.98, -0.09) (0.87, -0.20) (0.56, -0.01) Signed-HITS (0.37, 0.34) (0.32, 0.25) (0.35, 0.22) (0.93, 0.29) (0.86, 0.38) (0.57, 0.17) Bias and Deserve (0.36, 0.”
ActiveTrust: Secure and Trustable Routing in Wireless Sensor Networks (2016, background): “There is much related research, such as [20, 21, 22, 23, 24].”
Knowing What to Believe (when you already know something) (2010, background): “Reputation-based systems determine an entity’s trust or standing among peers via transitive recommendations, as PageRank (Brin and Page, 1998) does among web pages, Advogato (Levien, 2008) does among people, and Eigentrust (Kam-var et al., 2003) does among peers in a network.”
Sybil-Resilient Online Content Voting (2009, background): “In EigenTrust [11] and Credence [25], each user independently computes personalized reputation values for all users based on past trans-actions or voting histories.”
A survey of trust in computer science and the Semantic Web (2007, methodology): “…2005) [56] (Guha et al., 2004) [57] (Advogato, 2000) [58] (Chirita et al., 2004) [59] (Ding et al., 2004) [60] Trust in P2P networks and grids (Kamvar et al., 2003) [47] (Cornelli et al., 2002) [48] (Aberer and Despotovic, 2001) [49] (Damiani et al., 2002) [50] (Olmedilla et al., 2005) [7]…”
Integro: Leveraging Victim Prediction for Robust Fake Account Detection in OSNs (2015, methodology): “We chose SybilRank because it was shown to outperform known contenders [13], including EigenTrust [15], SybilGuard [16], SybilLimit [17], SybilInfer [18], Mislove’s method [19], and GateKeeper [20].”
Hinge-Loss Markov Random Fields and Probabilistic Soft Logic (2015, methodology): “…random fold splits are not the same, using the same experimental setup, Huang et al. (2013) also scored the precision-recall area for negative trust of standard trust prediction algorithms EigenTrust (Kamvar et al., 2003) and TidalTrust (Golbeck, 2005), which scored 0.131 and 0.130, respectively.”
Taxonomy and Survey of Collaborative Intrusion Detection (2015, methodology): “ACM Computing Surveys, Vol Taxonomy and Survey of Collaborative Intrusion Detection 55:7 A generic countermeasure against malicious insiders in distributed systems is the adoption of reputation systems [Resnick et al. 2000; Marti and Garcia-Molina 2006], such as EigenTrust [Kamvar et al. 2003].”
Adversarial Web Search (2011, background): “A well-known example of a mechanism for trust computation is EigenTrust [119] which is shown in simulations to reduce the ratio of inauthentic downloads in P2P networks with malicious peers.”