A Survey on Hyperlink Prediction
This is an incremental survey that organizes and compares existing methods for hyperlink prediction, which is useful for researchers in fields like chemical networks and social systems.
The paper provides a systematic survey on hyperlink prediction, proposing a new taxonomy to classify methods and conducting a benchmark study that shows deep learning-based methods outperform others.
As a natural extension of link prediction on graphs, hyperlink prediction aims for the inference of missing hyperlinks in hypergraphs, where a hyperlink can connect more than two nodes. Hyperlink prediction has applications in a wide range of systems, from chemical reaction networks, social communication networks, to protein-protein interaction networks. In this paper, we provide a systematic and comprehensive survey on hyperlink prediction. We propose a new taxonomy to classify existing hyperlink prediction methods into four categories: similarity-based, probability-based, matrix optimization-based, and deep learning-based methods. To compare the performance of methods from different categories, we perform a benchmark study on various hypergraph applications using representative methods from each category. Notably, deep learning-based methods prevail over other methods in hyperlink prediction.