document: Modeling the temporal dynamics of social rating networks using bidirectional effects of social relations and rating patterns A social rating network ( SRN ) is a social network in which edges represent social relationships and users ( nodes ) express ratings on some of the given items . Such networks play an increasingly important role in reviewing websites such as Epinions.com or online sharing websites like Flickr.com . In this paper , we first observe and analyze the temporal behavior of users in a social rating network , who express ratings and create social relations . Then , we model the temporal dynamics of an SRN based on our observations , using the bidirectional effects of ratings and social relations . While existing models for other types of social networks have captured some of the effects , our model is the first one to represent all four effects , i.e. social relations-on-ratings ( social influence ) , social relations-on-social relations ( transitivity ) , ratings-on-social relations ( selection ) , and ratings-on-ratings ( correlational influence ) . Existing works consider these effects as static and constant throughout the evolution of an SRN , however our observations reveal that these effects are actually dynamic . We propose a probabilistic generative model for SRNs , which models the strength and dynamics of each effect throughout the network evolution . This model can serve for the prediction of future links , ratings or community structures . Due to the sensitive nature of SRNs , another motivation for our work is the generation of synthetic SRN data sets for research purposes . Our experimental studies on two real life datasets ( Epinions and Flickr ) demonstrate that the proposed model produces social rating networks that agree with real world data on a comprehensive set of evaluation criteria .
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