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    Classifying networked entities with modularity kernels

    Zhang, Dell and Mao, R. (2008) Classifying networked entities with modularity kernels. In: Shanahan, J.G. and Amer-Yahia, S. and Manolescu, I. and Zhang, Y. and Evans, D.A. and Kolcz, A. and Choi, K.-S. and Chowdhury, A. (eds.) Proceeding of the 17th ACM conference on Information and knowledge mining - CIKM '08. New York, U.S.: ACM Press, pp. 113-122. ISBN 9781595939913.

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    Abstract

    Statistical machine learning techniques for data classification usually assume that all entities are i.i.d. (independent and identically distributed). However, real-world entities often interconnect with each other through explicit or implicit relationships to form a complex network. Although some graph-based classification methods have emerged in recent years, they are not really suitable for complex networks as they do not take the degree distribution of network into consideration. In this paper, we propose a new technique, Modularity Kernel, that can effectively exploit the latent community structure of networked entities for their classification. A number of experiments on hypertext datasets show that our proposed approach leads to excellent classification performance in comparison with the state-of-the-art methods.

    Metadata

    Item Type: Book Section
    Additional Information: Proceedings of the 17th ACM Conference on Information and Knowledge Management, CIKM 2008, Napa Valley, California, USA, October 26-30, 2008
    School: Birkbeck Schools and Departments > School of Business, Economics & Informatics > Computer Science and Information Systems
    Research Centre: Birkbeck Knowledge Lab
    Depositing User: Administrator
    Date Deposited: 30 May 2013 09:07
    Last Modified: 02 Dec 2016 13:26
    URI: http://eprints.bbk.ac.uk/id/eprint/7081

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