  In social network science, Facebook is one of the most interesting and widely
used social networks and media platforms. Its data contributed to significant
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 study
performance and evaluate multiple machine learning algorithms on different
feature sets. To derive 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 Facebook100 network,
but rarely found in other datasets. The adopted approaches are discussed and
results are clearly presented. Lastly, we compare and review applied models,
where overall performance and classification rates are presented.
