No abstract

In social network science, Facebook is one of the most interesting and widely used social networks and media platforms. Its data has significantly contributed to the evolution of social network research and link prediction techniques, which are important tools in link mining and analysis. This paper gives the first comprehensive analysis of link prediction on the Facebook100 network. We stu- dy performance and evaluate multiple machine learning algorithms on different feature sets. To derive the features, we use network embeddings and topology-based techniques such as node2vec and vectors of similarity metrics. In addition, we also employ node- -based features, which are available for the Facebook100 network, though rarely found in other datasets. The adopted approaches are discussed and results are clearly presented. Lastly, we compare and review the applied models, where overall performance and classification rates are presented.

In social network science, Facebook is one of the most interesting and widely used social networks and media platforms. Its data has significantly contributed to the evolution of social network research and link prediction techniques, which are important tools in link mining and analysis. This paper gives the first comprehensive analysis of link prediction on the Facebook100 network. We stu- dy performance and evaluate multiple machine learning algorithms on different feature sets. To derive the features, we use network embeddings and topology-based techniques such as node2vec and vectors of similarity metrics. In addition, we also employ node- -based features, which are available for the Facebook100 network, though rarely found in other datasets. The adopted approaches are discussed and results are clearly presented. Lastly, we compare and review the applied models, where overall performance and classification rates are presented.