Mining diversity on social media networks |
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Authors: | Lu Liu Feida Zhu Meng Jiang Jiawei Han Lifeng Sun Shiqiang Yang |
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Affiliation: | (1) Tsinghua University, Capital Medical University, Beijing, China;(2) Tsinghua University, Beijing, China;(3) Singapore Management University, 81 Victoria St., Singapore, Singapore;(4) University of Illinois at Urbana-Champaign, Urbana, IL, USA |
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Abstract: | The fast development of multimedia technology and increasing availability of network bandwidth has given rise to an abundance
of network data as a result of all the ever-booming social media and social websites in recent years, e.g., Flickr, Youtube,
MySpace, Facebook, etc. Social network analysis has therefore become a critical problem attracting enthusiasm from both academia
and industry. However, 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. Based on
the approach, we can measure not only a user’s sociality and interest diversity but also a social media’s user diversity.
An efficient top-k diversity ranking algorithm is developed for computation on dynamic networks. Experiments on both synthetic
and real social media datasets give interesting results, where individual nodes identified with high diversities are intuitive. |
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