Classifying networked entities with modularity kernels

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. Categories and Subject Descriptors H.2.8 [Database Management]: Database Applications-data mining; H.3.3 [Information Storage and Retrieval]: Information Search and Re...

• Type: Conference
• Year: 2008
• Where: CIKM
• Authors: Dell Zhang, Robert Mao
• Keywords: CIKM 2008, Complex Networks, Entities, Graph-based Classification Methods, Information Management