通过本地信息预测缺失的链路
Predicting Missing Links via Local Information
摘要：
网络的缺失链预测在现代科学中具有重要的理论意义和实际意义。在本文中，我们实证研究的基础上，节点相似性的链接预测的一个简单的框架。我们比较了九个著名的本地相似性措施在六个真实的网络。结果表明，最简单的措施，即共同的邻居，具有最好的整体性能，和Adamic-Adar指数表现第二好。提出了一种新的相似性度量，动机的资源分配过程中发生的网络，并显示出具有更高的预测精度比共同的邻居。研究发现，如果只使用最近邻的信息，许多链接被分配相同的分数。因此，我们设计了另一种新的措施，利用信息的次近邻，它可以显着提高预测精度。
英文摘要：
Missing link prediction of networks is of both theoretical interest and practical significance in modern science. In this paper, we empirically investigate a simple framework of link prediction on the basis of node similarity. We compare nine well-known local similarity measures on six real networks. The results indicate that the simplest measure, namely common neighbors, has the best overall performance, and the Adamic-Adar index performs the second best. A new similarity measure, motivated by the resource allocation process taking place on networks, is proposed and shown to have higher prediction accuracy than common neighbors. It is found that many links are assigned same scores if only the information of the nearest neighbors is used. We therefore design another new measure exploited information of the next nearest neighbors, which can remarkably enhance the prediction accuracy.
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。
