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. © Springer-Verlag Berlin Heidelberg 2010.
CITATION STYLE
Liu, L., Zhu, F., Chen, C., Yan, X., Han, J., Yu, P., & Yang, S. (2010). Mining diversity on networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5981 LNCS, pp. 384–398). https://doi.org/10.1007/978-3-642-12026-8_30
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