The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Diffusion Kernels on Graphs and Other Discrete Input Spaces
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
SIAM Journal on Discrete Mathematics
Algorithms for estimating relative importance in networks
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Evaluation of kernel-based link analysis measures on research paper recommendation
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Diffusion and graph spectral methods for network forensic analysis
NSPW '06 Proceedings of the 2006 workshop on New security paradigms
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Credit scoring algorithm based on link analysis ranking with support vector machine
Expert Systems with Applications: An International Journal
Graph nodes clustering with the sigmoid commute-time kernel: A comparative study
Data & Knowledge Engineering
On the properties of von Neumann kernels for link analysis
Machine Learning
The slashdot zoo: mining a social network with negative edges
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Learning spectral graph transformations for link prediction
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Graph-based analysis of semantic drift in Espresso-like bootstrapping algorithms
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Enhancing link-based similarity through the use of non-numerical labels and prior information
Proceedings of the Eighth Workshop on Mining and Learning with Graphs
The link prediction problem in bipartite networks
IPMU'10 Proceedings of the Computational intelligence for knowledge-based systems design, and 13th international conference on Information processing and management of uncertainty
Link prediction: the power of maximal entropy random walk
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Exploring multiple communities with kernel-based link analysis
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
One-Class support vector machines for recommendation tasks
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Directed laplacian kernels for link analysis
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Interest prediction on multinomial, time-evolving social graphs
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Predicting positive and negative links in signed social networks by transfer learning
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Co-occurrence prediction in a large location-based social network
Frontiers of Computer Science: Selected Publications from Chinese Universities
Prediction in a microblog hybrid network using bonacich potential
Proceedings of the 7th ACM international conference on Web search and data mining
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The application of kernel methods to link analysis is explored. In particular, Kandola et al.'s Neumann kernels are shown to subsume not only the co-citation and bibliographic coupling relatedness but also Kleinberg's HITS importance. These popular measures of relatedness and importance correspond to the Neumann kernels at the extremes of their parameter range, and hence these kernels can be interpreted as defining a spectrum of link analysis measures intermediate between co-citation/bibliographic coupling and HITS. We also show that the kernels based on the graph Laplacian, including the regularized Laplacian and diffusion kernels, provide relatedness measures that overcome some limitations of co-citation relatedness. The property of these kernel-based link analysis measures is examined with a network of bibliographic citations. Practical issues in applying these methods to real data are discussed, and possible solutions are proposed.