结合二层节点度和聚类系数的链路预测算法

研究复杂网络的链路预测算法对分析舆论传播方向、预测舆论演进趋势和控制舆论发展进程具有重要意义。针对现有的基于节点度的链路预测算法存在预测质量偏低的问题，提出了一种结合二层节点度和聚类系数的链路预测算法。算法全面考虑网络局部结构信息以及共同邻居节点之间的差异性，在相似性评价指标的选择上将节点度和聚类系数结合，深度挖掘节点相似性性质并将节点度扩展到二层。最后在三个真实数据集中分别进行仿真实验，结果表明提出的算法相比于Common Neighbors、Adamic-Adar和Resource Allocation等经典算法具有更好的性能。

Link Prediction Algorithm Combining Two-Layer Node Degree and Clustering Coefficient

It is important to study link prediction algorithms for complex networks to analyze the direction of public opinion communication, predicting the trend of public opinion evolution, and controlling the development of public opinion. To solve the problem that link prediction algorithm based on node degree has low quality of prediction, a link prediction method based on two-layer degree of code and clustering coefficient is proposed. The algorithm comprehensively considers the local structure information of the network and the difference between the common neighbor nodes. The node degree and the clustering coefficient are combined in the selection of the similarity evaluation index, and the similarity property of the deep mining node extends the node degree to two layers. Finally, simulation experiments are carried out in three real data sets. The results show that the proposed algorithm has better performance than classical algorithms such as Common Neighbors, Adamic-Adar and Resource Allocation.