Link Prediction Method Fusion with Local Structural Entropy for Directed Network

Link prediction utilizes accessible network information to complement or predict the network links. Similarity is an important prerequisite for link prediction which means links more likely occurs between two similar nodes. Existing methods utilize the similarity of nodes but neglect of network structure. However the link direction leads to a far more complex structure and contains more information useful than the undirected networks. Most classic methods are difficult to depict the distribution of the network structure with incidental direction so the similarity characteristics of the network structure itself are lost. In this respect, a new method of local structure entropy is proposed to depict the directed structural distribution characteristics, which can be used to evaluate the degree of local structural similarity of nodes and then applied to link prediction methods. Experimental results on 8 real directed networks show that this method is effective for both area under the receiver operating characteristic curve (AUC) and ranking-score measures, and improved predictive capacity of the baseline methodology.