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A new kernel for classification of networked entities

Zhang, Dell and Mao, R. (2008) A new kernel for classification of networked entities. In: 6th International Workshop on Mining and Learning with Graphs, 4-5 July 2008, Helsinki, Finland. (Unpublished)

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"Statistical machine learning techniques for data classi cation 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 classi cation. 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."

Item Type: Conference or Workshop Item (Paper)
School or Research Centre: Birkbeck Schools and Research Centres > School of Business, Economics & Informatics > Computer Science and Information Systems
Depositing User: Administrator
Date Deposited: 15 Sep 2010 10:44
Last Modified: 17 Apr 2013 12:17

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