The aim of trust prediction is to infer trust values for pairs of users when the relationship between them is unknown. The unprecedented growth in the amount of online interactions on e-commerce websites has made the problem of predicting user trust relationships crit-ically important, yet sparsity in the amount of known (labeled) relationships poses a significant challenge to the usage of machine learning techniques. This pa-per presents a community detection approach which leverages the network of available trust relations and rating similarities to compensate for the lack of labels. The key insight behind our framework is that trust values from the central community members can be used as a predictor for relationships between other community members. Here we evaluate the usage of two community detection algorithms, one of which works merely on the trust network while the other one uses both. Our algorithm outperforms other existing trust prediction methods on datasets from the well-known product review websites Epinions and Ciao. I

Abstract—Most real-world social networks are inherently dynamic and composed of communities that are constantly changing in membership. As a result, recent years have witnessed increased attention toward the challenging problem of detecting evolving communities. This paper presents a game-theoretic approach for community detection in dynamic social networks in which each node is treated as a rational agent who periodically chooses from a set of predefined actions in order to maximize its utility function. The community structure of a snapshot emerges after the game reaches Nash equilibrium; the partitions and agent information are then transferred to the next snapshot. An evaluation of our method on two real world dynamic datasets (AS-Internet Routers Graph and AS-Oregon Graph) demonstrates that we are able to report more stable and accurate communities over time compared to the benchmark methods. Keywords-community detection; dynamic social networks; game-theoretic models I.