Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
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
Propagation of trust and distrust
Proceedings of the 13th international conference on World Wide Web
PeerTrust: Supporting Reputation-Based Trust for Peer-to-Peer Electronic Communities
IEEE Transactions on Knowledge and Data Engineering
Fast discovery of connection subgraphs
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Graphs over time: densification laws, shrinking diameters and possible explanations
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Propagation Models for Trust and Distrust in Social Networks
Information Systems Frontiers
Measuring and extracting proximity in networks
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Inferring binary trust relationships in Web-based social networks
ACM Transactions on Internet Technology (TOIT)
PowerTrust: A Robust and Scalable Reputation System for Trusted Peer-to-Peer Computing
IEEE Transactions on Parallel and Distributed Systems
Fast direction-aware proximity for graph mining
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Finding the k shortest simple paths: A new algorithm and its implementation
ACM Transactions on Algorithms (TALG)
Finding reliable subgraphs from large probabilistic graphs
Data Mining and Knowledge Discovery
Operators for propagating trust and their evaluation in social networks
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Trust representation and aggregation in a distributed agent system
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Controversial users demand local trust metrics: an experimental study on Epinions.com community
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
SUNNY: a new algorithm for trust inference in social networks using probabilistic confidence models
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Formal trust model for multiagent systems
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Trust Inference in Complex Trust-Oriented Social Networks
CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 04
Fast Discovery of Reliable Subnetworks
ASONAM '10 Proceedings of the 2010 International Conference on Advances in Social Networks Analysis and Mining
Multi-dimensional evidence-based trust management with multi-trusted paths
Future Generation Computer Systems
Comparing linkage graph and activity graph of online social networks
SocInfo'11 Proceedings of the Third international conference on Social informatics
Assessing Trust by Disclosure in Online Social Networks
ASONAM '11 Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining
MATRI: a multi-aspect and transitive trust inference model
Proceedings of the 22nd international conference on World Wide Web
Enhancing trustworthiness evaluation in internetware with similarity and non-negative constraints
Proceedings of the 5th Asia-Pacific Symposium on Internetware
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Trust inference is an essential task in many real world applications. Most of the existing inference algorithms suffer from the scalability issue, making themselves computationally costly, or even infeasible, for the graphs with more than thousands of nodes. In addition, the inference result, which is typically an abstract, numerical trustworthiness score, might be difficult for the end-user to interpret. In this paper, we propose sub graph extraction to address these challenges. The core of the proposed method consists of two stages: path selection and component induction. The outputs of both stages can be used as an intermediate step to speed up a variety of existing trust inference algorithms. Our experimental evaluations on real graphs show that the proposed method can accelerate existing trust inference algorithms, while maintaining high accuracy. In addition, the extracted sub graph provides an intuitive way to interpret the resulting trustworthiness score.