关键词: 复杂网络, 链路预测, 集体影响, 聚类信息, 贝叶斯网络, 相似性算法

Abstract:

The task of the link prediction is to exploit the missing links in the network or to predict the possibility of a link between a pair of nodes. How to predict missing link accurately in incomplete networks is a challenging problem. Considering both the collective influence of nodes and the contribution of edge clustering information, this paper puts forward a new link prediction algorithm named CELP (link prediction algorithm based on collective influence and edge clustering information). Then, combining the community attributes of the node and based on the designed Bayesian network, this paper proposes the extended algorithm CELP* for label network. The experimental results of multiple test networks in various fields demonstrate that compared with the typical link prediction methods and recent partial algorithms, the method improves the prediction accuracy while maintaining the same AUC level, and further confirms the importance of local node information and edge information for link prediction.

Key words: complex network, link prediction, collective influence, clustering information, Bayesian network, similarity algorithm