Enhanced hypertext categorization using hyperlinks
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Learning to extract symbolic knowledge from the World Wide Web
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Low-Rank Matrix Approximation Using the Lanczos Bidiagonalization Process with Applications
SIAM Journal on Scientific Computing
Automating the Construction of Internet Portals with Machine Learning
Information Retrieval
Journal of Intelligent Information Systems
A Study of Approaches to Hypertext Categorization
Journal of Intelligent Information Systems
Hyperlink Analysis for the Web
IEEE Internet Computing
Composite Kernels for Hypertext Categorisation
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Linkage and Autocorrelation Cause Feature Selection Bias in Relational Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Diffusion Kernels on Graphs and Other Discrete Input Spaces
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Effectively Finding Relevant Web Pages from Linkage Information
IEEE Transactions on Knowledge and Data Engineering
Building a web thesaurus from web link structure
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Dependency networks for inference, collaborative filtering, and data visualization
The Journal of Machine Learning Research
Learning probabilistic models of link structure
The Journal of Machine Learning Research
Mining Web Informative Structures and Contents Based on Entropy Analysis
IEEE Transactions on Knowledge and Data Engineering
A survey of kernels for structured data
ACM SIGKDD Explorations Newsletter
Clustering Large Graphs via the Singular Value Decomposition
Machine Learning
Probabilistic classification and clustering in relational data
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
When are links useful? experiments in text classification
ECIR'03 Proceedings of the 25th European conference on IR research
Numerical Linear Algebra and Applications, Second Edition
Numerical Linear Algebra and Applications, Second Edition
Discriminative probabilistic models for relational data
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Learning Contextual Dependency Network Models for Link-Based Classification
IEEE Transactions on Knowledge and Data Engineering
Cautious Collective Classification
The Journal of Machine Learning Research
Robust collective classification with contextual dependency network models
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Supervised word sense disambiguation using semantic diffusion kernel
Engineering Applications of Artificial Intelligence
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Generally, links among objects demonstrate certain patterns and contain rich semantic clues. These important clues can be used to improve classification accuracy. However, many real-world link data may exhibit more complex regularity. For example, there may be some noisy links that carry no human editorial endorsement about semantic relationships. To effectively capture such regularity, this paper proposes latent linkage semantic kernels (LLSKs) by first introducing the linkage kernels to model the local and global dependency structure of a link graph and then applying the singular value decomposition (SVD) in the kernel-induced space. For the computational efficiency on large datasets, we also develop a block-based algorithm for LLSKs. A kernel-based contextual dependency network (KCDN) model is then presented to exploit the dependencies in a network of objects for collective classification. We provide experimental results demonstrating that the KCDN model, together with LLSKs, demonstrates relatively high robustness on the datasets with the complex link regularity, and the block-based computation method can scale well with varying sizes of the problem.