Learning-based link prediction analysis for Facebook100 network

TL;DR

This paper provides a comprehensive analysis of link prediction on the Facebook100 network, evaluating multiple machine learning algorithms using network embeddings, topology-based, and node-based features.

Contribution

It is the first to systematically analyze link prediction performance on Facebook100, combining diverse feature sets and models for social network analysis.

Findings

Network embeddings improve link prediction accuracy

Node-based features enhance model performance

Comparative analysis of machine learning algorithms

Abstract

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.