基于互信息的复杂网络链接预测
Link Prediction in Complex Networks: A Mutual Information Perspective
摘要：
近年来，网络的拓扑性质被广泛应用于链路预测问题的研究。例如，共同邻居是一个自然而有效的框架。因此，已经提出了许多变体的共同邻居，以进一步提高候选链接的判别分辨率。本文从信息论的角度重新审视了网络拓扑在预测丢失链路中的作用，并提出了一种基于网络结构互信息的实用方法。该方法不仅可以大幅度提高预测精度，而且计算复杂度也比较合理.
英文摘要：
Topological properties of networks are widely applied to study the link-prediction problem recently. Common Neighbors, for example, is a natural yet efficient framework. Many variants of Common Neighbors have been thus proposed to further boost the discriminative resolution of candidate links. In this paper, we reexamine the role of network topology in predicting missing links from the perspective of information theory, and present a practical approach based on the mutual information of network structures. It not only can improve the prediction accuracy substantially, but also experiences reasonable computing complexity.
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。
