Learning-based link prediction analysis for Facebook100 network

In social network science, Facebook is one of the most interesting and widely used social networks and media platforms. In the previous decade Facebook data contributed to significant evolution of social network research. Paired with this topic we have experienced growing popularity in the link prediction techniques, which are important tools in link mining and analysis. This paper gives a comprehensive overview of link prediction analysis on the Facebook100 network, which was derived in 2005. We study performance and evaluate multiple machine learning algorithms on this network. We use networks embeddings and topology-based techniques such as node2vec and vectors of similarity metrics. Using these techniques similarity features for our classification models are derived. Further we discuss our approach and present results. Lastly, we compare and review our models, where overall performance and classification rates are presented.

An expert system for detecting automobile insurance fraud using social network analysis

The article proposes an expert system for detection, and subsequent investigation, of groups of collaborating automobile insurance fraudsters. The system is described and examined in great detail, several technical difficulties in detecting fraud are also considered, for it to be applicable in practice. Opposed to many other approaches, the system uses networks for representation of data. Networks are the most natural representation of such a relational domain, allowing formulation and analysis of complex relations between entities. Fraudulent entities are found by employing a novel assessment algorithm, \textit{Iterative Assessment Algorithm} (\textit{IAA}), also presented in the article. Besides intrinsic attributes of entities, the algorithm explores also the relations between entities. The prototype was evaluated and rigorously analyzed on real world data. Results show that automobile insurance fraud can be efficiently detected with the proposed system and that appropriate data representation is vital.