文章基本信息

标题：One model to rule them all in network science?
本地全文：下载
作者：Roger Guimerà
期刊名称：Proceedings of the National Academy of Sciences
印刷版ISSN：0027-8424
电子版ISSN：1091-6490
出版年度：2020
卷号：117
期号：41
页码：25195-25197
DOI：10.1073/pnas.2017807117
出版社：The National Academy of Sciences of the United States of America
摘要：If you have ever used a social network platform, you know that you are regularly prompted about people you may know in the network. Sometimes these recommendations are striking—we get a suggestion for a person we have met only once, or an old acquaintance that we have not seen in years. How could anyone (let alone a computer) possibly guess? Predicting acquaintances in a social network is just one example of the general problem of link prediction (1⇓⇓⇓–5), which consists of predicting connections (links) in a network (6) from the observation of other connections (Fig. 1). Besides social networking, the problem of link prediction occurs in many contexts, from recommender systems, where customers are recommended (linked to) items based on their previous ratings or purchases (7), to the prediction of unknown harmful (or perhaps synergistic) interactions between drugs (8).