Mining Diversity on Networks

• Main Authors: LIU, Lu, ZHU, Feida, CHEN, Chen, YAN, Xifeng, HAN, Jiawei, YU, Philip, YANG, Shiqiang
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2010
• Subjects: Databases and Information Systems; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/509; https://ink.library.smu.edu.sg/context/sis_research/article/1508/viewcontent/MiningDiversityonNetworks_2010.pdf
• Institution: Singapore Management University

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, we give a comprehensive study of this concept. We 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.