Mining Diversity on Networks
Source: University of Illinois
Despite the recent emergence of many large-scale networks in different application domains, an important measure that captures a participant's diversity in the network has been largely neglected in previous studies. Namely, diversity characterizes how diverse a given node connects with its peers. In this paper, the authors give a comprehensive study of this concept. They first lay out two criteria that capture the semantic meaning of diversity, and then propose a compliant definition which is simple enough to embed the idea. An efficient top-k diversity ranking algorithm is developed for computation on dynamic networks. Experiments on both synthetic and real datasets give interesting results, where individual nodes identified with high diversities are intuitive.