A HeteSim-Measured algorithm fused semantic features in heterogeneous social networks

The construction of heterogeneous social networks enables the major social platforms in the network to connect through social information. In order to ensure network security and improve downstream tasks such as user profile, knowledge graph construction and recommendation, the relevance measurement between social information has attracted extensive attention in recent years. Although HeteSim algorithm has achieved good results in measuring the relevance between heterogeneous nodes, this method only focuses on the structure features between nodes, and fails to comprehensively consider the joint impact of structure features and semantic features. Therefore, this paper proposes HeteSim-Measured algorithm that considers the fusion of structure features and semantic features for improving the accuracy of relevance measurement. The experiment is verified by measuring the relevance based on meta-path on the datasets and comparing with HeteSim algorithm.