Extracting reputation in multi agent systems by means of social network topology
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
Reputation and social network analysis in multi-agent systems
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
Data Mining and Knowledge Discovery
The Eigentrust algorithm for reputation management in P2P networks
WWW '03 Proceedings of the 12th international conference on World Wide Web
A Computational Model of Trust and Reputation for E-businesses
HICSS '02 Proceedings of the 35th Annual Hawaii International Conference on System Sciences (HICSS'02)-Volume 7 - Volume 7
Social ReGreT, a reputation model based on social relations
ACM SIGecom Exchanges - Chains of commitment
The Knowledge Engineering Review
Propagation Models for Trust and Distrust in Social Networks
Information Systems Frontiers
An integrated trust and reputation model for open multi-agent systems
Autonomous Agents and Multi-Agent Systems
Classification in Networked Data: A Toolkit and a Univariate Case Study
The Journal of Machine Learning Research
A statistical relational model for trust learning
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
GossipTrust for Fast Reputation Aggregation in Peer-to-Peer Networks
IEEE Transactions on Knowledge and Data Engineering
Smart cheaters do prosper: defeating trust and reputation systems
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Social and Economic Networks
Within-Network Classification Using Local Structure Similarity
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Cautious inference in collective classification
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Formal trust model for multiagent systems
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Extracting and visualizing trust relationships from online auction feedback comments
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Social network classification incorporating link typevalues
ISI'09 Proceedings of the 2009 IEEE international conference on Intelligence and security informatics
ICML'06 Proceedings of the 2006 conference on Statistical network analysis
Bootstrapping trust evaluations through stereotypes
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Label-dependent feature extraction in social networks for node classification
SocInfo'10 Proceedings of the Second international conference on Social informatics
A method of label-dependent feature extraction in social networks
ICCCI'10 Proceedings of the Second international conference on Computational collective intelligence: technologies and applications - Volume Part II
Identifying influential agents for advertising in multi-agent markets
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
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In this paper, we present a collective classification approach for identifying untrustworthy individuals in multi-agent communities from a combination of observable features and network connections. Under the assumption that data are organized as independent and identically distributed (i.i.d.)samples, traditional classification is typically performed on each object independently, without considering the underlying network connecting the instances. In collective classification, a set of relational features, based on the connections between instances, is used to augment the feature vector used in classification. This approach can perform particularly well when the underlying data exhibits homophily, a propensity for similar items to be connected. We suggest that in many cases human communities exhibit homophily in trust levels since shared attitudes toward trust can facilitate the formation and maintenance of bonds, in the same way that other types of shared beliefs and value systems do. Hence, knowledge of an agent's connections provides a valuable cue that can assist in the identification of untrustworthy individuals who are misrepresenting themselves by modifying their observable information. This paper presents results that demonstrate that our proposed trust evaluation method is robust in cases where a large percentage of the individuals present misleading information.