A Noise-Filtering Method for Link Prediction in Complex Networks.
A Noise-Filtering Method for Link Prediction in Complex Networks.
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用于复杂网络中链接预测的噪声过滤方法。
DOI：
10.1371/journal.pone.0146925
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发表时间：
2016
期刊：
影响因子：
3.7
通讯作者：
Teng Z
中科院分区：
文献类型：
--
作者：
Ouyang B;Jiang L;Teng Z
链接预测在寻找网络系统中的缺失链接和补充我们对网络演化的理解方面起着重要作用。如何根据观测到的拓扑结构有效地预测丢失的/未来的链路是网络科学界关注的焦点。真实世界的信息总是包含噪声，在观测网络中也是如此。在现有的方法中很少考虑这个问题。在本文中，我们把观察到的链接的存在性作为已知信息。通过过滤掉这些信息中的噪声，检索连接信息的潜在规律性，然后用于预测丢失或未来的链接。在各种经验网络上的实验表明，我们的方法的性能明显优于基线算法。
Link prediction plays an important role in both finding missing links in networked systems and complementing our understanding of the evolution of networks. Much attention from the network science community are paid to figure out how to efficiently predict the missing/future links based on the observed topology. Real-world information always contain noise, which is also the case in an observed network. This problem is rarely considered in existing methods. In this paper, we treat the existence of observed links as known information. By filtering out noises in this information, the underlying regularity of the connection information is retrieved and then used to predict missing or future links. Experiments on various empirical networks show that our method performs noticeably better than baseline algorithms.