Association rule mining and applications based on higher-order structures in complex networks
The study of higher-order structures,which refer to subnetworks within a network,is a crucial research topic in network science.In recent years,although the research on higher-order structures has been increasing,there has been relatively little research on the internal connections between higher-order structures.In light of conventional association rules,the evaluation criteria of association rules between higher-order structures are defined,and a general algorithm framework for effectively mining these association rules is proposed.The proposed approach has been applyed to mine association rules among three-order structures in six real-world networks.The results demonstrate strong association rules between higher-order structures in real-world networks,as well as variations in these rules across different networks.Additionally,we apply strong association rules to link prediction,resulting in a new link prediction method.This method outperforms the baseline methods in four real networks.
association rulecomplex networkhigher-order structurelink prediction
万方数据
维普
