Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Learning to Probabilistically Identify Authoritative Documents
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Application of kernels to link analysis
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
IEEE Transactions on Knowledge and Data Engineering
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
On the properties of von Neumann kernels for link analysis
Machine Learning
Hi-index | 0.01 |
We compare various kernel-based link analysis measures on graph nodes to evaluate their utility as a research paper recommendation system. The compared measures include the Kandola et al.'s von Neumann kernel, its extension that takes communities into account, and Smola and Kondor's regularized Laplacian. Chebotarev and Shamis' matrix forest-based algorithm, Kleinberg's HITS authority ranking, and classic co-citation coupling are also evaluated. The experimental result shows that kernel-based methods outperform HITS and co-citation coupling, with the community-based von Neumann kernel achieving the highest score.