基于路径熵的链路预测
Link prediction based on path entropy
摘要：
信息论是一种很有前途的量化复杂网络复杂性的工具。本文首先用信息论的方法研究了路径的信息熵或不确定性。然后，我们将路径熵应用于现实网络中的链接预测问题。具体来说，我们提出了一个新的相似性指数，即路径熵（PE）指数，它考虑了节点对之间的最短路径的信息熵与惩罚长路径。实证实验表明，PE指数优于主流的链接预测。
英文摘要：
Information theory has been taken as a prospective tool for quantifying the complexity of complex networks. In this paper, we first study the information entropy or uncertainty of a path using the information theory. Then we apply the path entropy to the link prediction problem in real-world networks. Specifically, we propose a new similarity index, namely Path Entropy (PE) index, which considers the information entropies of shortest paths between node pairs with penalization to long paths. Empirical experiments demonstrate that PE index outperforms the mainstream link predictors.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
