Dynamic Weight-Based Individual Similarity Calculation for Information Searching in Social Computing

摘要: In the social computing environment, complete information about an individual is usually distributed in heterogeneous networks, which are presented as linked data. Synthetically recognizing and integrating these data for efficiently searching important but challenging work. this paper, a dynamic weight (DW)-based similarity calculation proposed to recognize integrate similar individuals from environments. First, each link of weighted by applying DW. Then, semantic metric combine DW into calculation. system framework similarity-based designed tested real-world sets. Finally, massive experiments conducted both benchmark community The results show that our approach can produce good result networks. addition, it performs significantly better than existing state-of-the-art approaches searching.