Abstract
Eigentrust is a simple and popular method for trust computation, which uses both direct and indirect information about individual performance to provide a global trust rating. This final trust value is based on eigenvectors computed through the Power Method. However, under certain network topologies, the Power Method cannot be used to identify appropriate eigenvectors. After characterising these cases, we overcome Eigentrust’s limitations by extending the algorithm’s core ideas into the Max-Plus Algebra. An empirical evaluation of our new approach demonstrates its superiority to Eigentrust.
Original language | English |
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Title of host publication | ECAI 2020 |
Editors | Guiseppe De Giacomo, Alejandro Catala, Bistra Dilkina, Michela Milano, Senén Barro, Alberto Bugarín, Jérôme Lang |
Publisher | IOS Press |
Pages | 3-10 |
Number of pages | 8 |
ISBN (Electronic) | 978-1-64368-101-6 |
ISBN (Print) | 978-1-64368-100-9 |
DOIs | |
Publication status | Published - 2020 |
Event | 24th European Conference on Artificial Intelligence - Santiago de Compostela, Spain Duration: 8 Jun 2020 → 12 Jun 2020 http://ecai2020.eu/ |
Publication series
Name | Frontiers in Artificial Intelligence and Applications |
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Publisher | IOS Press |
ISSN (Print) | 0922-6389 |
ISSN (Electronic) | 1879-8314 |
Conference
Conference | 24th European Conference on Artificial Intelligence |
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Abbreviated title | ECAI 2020 |
Country/Territory | Spain |
City | Santiago de Compostela |
Period | 8/06/20 → 12/06/20 |
Internet address |