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.

One of the most important factors that contribute to the success of a machine learning model is a good training objective. Training objective crucially influences the model's performance and generalization capabilities. This paper specifically focuses on graph neural network training objective for link prediction, which has not been explored in the existing literature. Here, the training objective includes, among others, a negative sampling strategy, and various hyperparameters, such as edge message ratio which controls how training edges are used. Commonly, these hyperparameters are fine-tuned by complete grid search, which is very time-consuming and model-dependent. To mitigate these limitations, we propose Adaptive Grid Search (AdaGrid), which dynamically adjusts the edge message ratio during training. It is model agnostic and highly scalable with a fully customizable computational budget. Through extensive experiments, we show that AdaGrid can boost the performance of the models up to 1.9% while being nine times more time-efficient than a complete search. Overall, AdaGrid represents an effective automated algorithm for designing machine learning training objectives.