Predicting missing links via correlation between nodes

Hao Liao, An Zeng* and Yi-Cheng Zhang, Predicting missing links via correlation between nodes,

[Physica A 436, 216 (2015)]

简介：链路预测是复杂网络研究中的一个热门问题。现有大部分链路预测算法都是通过对网络中节点相似程度的估算来实现连边预测，其基本思想是假设相似的节点之间更有可能在未来存在连边。与已有工作不同，本文提出可以通过皮尔逊相关系数来对于节点相似程度进行估算。结果显示，这种方法能有效去除高阶路径中的噪音信息，因此在稀疏网络上能达到较高的链路预测精度。

摘要

As a fundamental problem in many different fields, link prediction aims to estimate the likelihood of an existing link between two nodes based on the observed information. Since this problem is related to many applications ranging from uncovering missing data to predicting the evolution of networks, link prediction has been intensively investigated recently and many methods have been proposed so far. The essential challenge of link prediction is to estimate the similarity between nodes. Most of the existing methods are based on the common neighbor index and its variants. In this paper, we propose to calculate the similarity between nodes by the Pearson correlation coefficient. This method is found to be very effective when applied to calculate similarity based on high order paths. We finally fuse the correlation-based method with the resource allocation method, and find that the combined method can substantially outperform the existing methods, especially in sparse networks.

原文链接：http://www.sciencedirect.com/science/article/pii/S0378437115004240