STAGE: Stereotypical Trust Assessment through Graph Extraction

Bootstrapping trust assessment where there is little or no evidence regarding a subject is a significant challenge for existing trust and reputation systems. When direct or indirect evidence is absent, existing approaches usually assume that all agents are equally trustworthy. This naive assumption makes existing approaches vulnerable to attacks such as Sybil and whitewashing. Inspired by real-life scenarios, we argue that malicious agents may share some common patterns or complex features in their descriptions. If such patterns or features can be detected, they can be exploited to bootstrap trust assessments. Based on this idea, we propose the use of frequent subgraph mining and state-of-the-art knowledge representation formalisms to estimate a priori trust for agents. Our approach first discovers significant patterns that may be used to characterize trustworthy and untrustworthy agents. Then, these patterns are used as features to train a regression model to estimate the trustworthiness of agents. Last, a priori trust for unknown agents (e.g., newcomers) is estimated using the discovered features based on the trained model. Through empirical evaluation, we show that the proposed approach significantly outperforms well-known trust approaches if trustworthiness of agents is correlated with patterns in their descriptions or social networks. Furthermore, we show that the proposed approach performs at least as good as the existing approaches if such correlations do not exist.