论文标题
关于超链接预测的调查
A Survey on Hyperlink Prediction
论文作者
论文摘要
作为图表上链路预测的自然扩展，超链接预测的目的是推断超图中缺失的超链接，其中超链接可以连接两个以上的节点。超链接预测在从化学反应网络，社交通信网络到蛋白质 - 蛋白质相互作用网络的广泛系统中具有应用。在本文中，我们提供了有关超链接预测的系统和全面调查。我们提出了一种新的分类法，将现有的超链接预测方法分类为四类：基于相似性的基于概率，基于矩阵优化的基于矩阵优化和基于深度学习的方法。为了比较来自不同类别的方法的性能，我们使用每个类别的代表性方法对各种超图应用进行了基准研究。值得注意的是，基于深度学习的方法比超链接预测中的其他方法占了上风。
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.
