Learning-based link prediction analysis for Facebook100 network
It provides a detailed benchmark for link prediction in social network analysis, but is incremental as it applies existing methods to a specific dataset.
This paper conducted the first comprehensive analysis of link prediction on the Facebook100 network, evaluating multiple machine learning algorithms with features from network embeddings, topology-based techniques, and node-based data, and presented overall performance and classification rates.
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.