Abstract
Link prediction has become a common way to infer new associations among actors in social networks. Most existing methods focus on the local and global information neglecting the implication of the actors in social groups. Further, the prediction process is characterized by a high complexity and uncertainty. In order to address these problems, we firstly introduce a new evidential weighted version of the social networks graph-based model that encapsulates the uncertainty at the edges level using the belief function framework. Secondly, we use this graph-based model to provide a novel approach for link prediction that takes into consideration both groups information and uncertainty in social networks. The performance of the method is experimented on a real world social network with group information and shows interesting results.
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Mallek, S., Boukhris, I., Elouedi, Z., Lefevre, E. (2015). Evidential Link Prediction Based on Group Information. In: Prasath, R., Vuppala, A., Kathirvalavakumar, T. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2015. Lecture Notes in Computer Science(), vol 9468. Springer, Cham. https://doi.org/10.1007/978-3-319-26832-3_45
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DOI: https://doi.org/10.1007/978-3-319-26832-3_45
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