Link Prediction Based on Clustering Coefficient
Abstract: Link prediction in complex network aims at estimating the likelihood of the existence of links be-tween nodes by the known network structure. Currently, most link prediction algorithms are si-milarity algorithms based on local information including the number of common neighbor nodes, degree of common neighbor nodes and the interactions between common neighbor nodes, and thus their applied range is limited. In this paper, we consider the interactions between adjacent nodes of a node and design a new algorithm based on clustering coefficient. We use this new algorithm in the experiments on real networks and simulative networks generated by pajek, and experimental results show that the algorithm is applicable to a wide range of problems and it has the high accuracy of prediction.
文章引用: 黄子轩 , 马 超 , 徐瑾辉 , 黄江楠 (2014) 基于集聚系数的链路预测算法。 应用物理， 4， 101-106. doi: 10.12677/APP.2014.46014
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