关键词: 复杂网络, 链路预测, 相似性, 聚类系数, 节点度

Abstract: 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.

Key words: complex network, link prediction, dissimilarity, clustering coefficient, node degree