The identification of communities is significant for the understanding of network structures and functions. Since some nodes naturally belong to several communities, the study of overlapping community structures has attracted increasing attention recently, and many algorithms have been designed to detect overlapping communities. We propose a new algorithm. The main idea is first to find the core of a community by detecting maximal cliques and then merging some tight community cores to form the community. Experimental results on two real networks demonstrate that the present algorithm is more accurate for detecting overlapping community structures, compared with some well-known results and methods.
Recently, collaborative tagging systems have attracted more and more attention and have been wlaely appnea in web systems. Tags provide highly abstracted information about personal preferences and item content, and therefore have the potential to help in improving better personalized recommendations, We propose a diffusion- based recommendation algorithm considering the personal vocabulary and evaluate it in a real-world dataset： Del.icio.us. Experimental results demonstrate that the usage of tag information can significantly improve the accuracy of personalized recommendations.
