首页|Link Prediction Method Fusion with Local Structural Entropy for Directed Network
Link Prediction Method Fusion with Local Structural Entropy for Directed Network
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
万方数据
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 clas-sic 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 pro-posed 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.
Local structural entropyStructural complexitySimilar nodesLink prediction
Shuxin LIU、Hongchang CHEN、Lan WU、Kai WANG、Xing LI
展开 >
People's Liberation Army Strategic Support Force Information Engineering University,Zhengzhou 450002,China
Major Science and Technology Projects in Henan Province
万方数据
