Diffusion Algorithms in Multimedia Social Networks: A Novel Model

Online social network (OSN) is quickly becoming a promising field of interest for big data analytics and for a number of real applications, ranging from marketing to user profiling and recommendation. Anyway, although OSNs are naturally formed by heterogeneous data, in the recent past, only a few works have considered an explicit use of multimedia in their models. In this paper, we explicitly take into account the intrinsic characteristics of multimedia, having the awareness that in this way, both the models and the analysis algorithms will enormously benefit from such kind of information. In particular, we describe a multimedia data model for OSN, in order to provide, in a unique framework, novel mechanisms for effective management of multimedia information supporting several classic applications, such as influence analysis and maximization. Experiments have been carried out on Flickr Creative Commons Y FCC100M dataset containing about 100 million images, showing that the proposed approach combines both time and efficacy performances.