On the properties of von Neumann kernels for link analysis (original) (raw)
References
Acharyya, S., & Ghosh, J. (2003). A maximum entropy framework for higher order link analysis on discrete graphs. In Workshop on link analysis for detecting complex behavior (LinkKDD 2003).
Baldi, P., Frasconi, P., & Smyth, P. (2003). Modeling the Internet and the web: probabilistic methods and algorithms. New York: Wiley. Google Scholar
Bharat, K., & Henzinger, M. R. (1998). Improved algorithms for topic distillation in a hyperlinked environment. In Proceedings of the 21st annual international ACM SIGIR conference on research and development in information retrieval, Melbourne, Australia.
Bollacker, K. D., Lawrence, S., & Giles, C. L. (1998). CiteSeer: an autonomous web agent for automatic retrieval and identification of interesting publications. In Proceedings of the 2nd international ACM conference on autonomous agents (pp. 116–123).
Brin, S., & Page, L. (1998). The anatomy of a large-scale hypertextual (web) search engine. Computer Network and ISDN Systems, 30(1–7), 107–117. Article Google Scholar
Chebotarev, P. Y., & Shamis, E. V. (1997). The matrix-foreset theorem and measuring relations in small social groups. Automation and Remote Control, 58(9), 1505–1514. MATHMathSciNet Google Scholar
Chung, F. R. K. (1997). Spectral graph theory. Providence: American Mathematical Society. MATH Google Scholar
Cohn, D., & Chang, H. (2000). Learning to probabilistically identify authoritative documents. In Proceedings of the 17th international conference on machine learning (ICML’00) (pp. 167–174), Stanford, CA, USA.
Cristianini, N., Kandola, J., & Elissee, A. (2002). On kernel-target alignment. In Advances in neural information processing systems (Vol. 14, pp. 367–373). Cambridge: MIT Press. Google Scholar
Dale, R., Moisl, H., & Somers, H. (Eds.). (2000). Handbook of natural language processing. New York: Marcel Dekker. Google Scholar
Deerwester, S. C., Dumais, S. T., Landauer, T. K., Furnas, G. W., & Harshman, R. A. (1990). Indexing by latent semantic analysis. Journal of the American Society for Information Science, 41(6), 391–407. Article Google Scholar
Dhyani, D., Ng, W. K., & Bhowmick, S. S. (2002). A survey of web metrics. ACM Computing Surveys, 34(4), 469–503. Article Google Scholar
Fagin, R., Kumar, R., & Sivakumar, D. (2003). Comparing top k lists. SIAM Journal on Discrete Mathematics, 17(1), 134–160. ArticleMATHMathSciNet Google Scholar
Fouss, F., Pirotte, A., Renders, J. M., & Saerens, M. (2007). Random-walk computation of similarities between nodes of a graph with application to collaborative recommendation. IEEE Transactions on Knowledge and Data Engineering, 19(3), 355–369. Article Google Scholar
Fouss, F., Yen, L., Dupont, P., & Saerens, M. (2006). An experimental investigation of graph kernels on a collaborative recommendation task. In Proceedings of the 2006 IEEE international conference on data mining (ICDM’06) (pp. 863–868), Hong Kong.
Haussler, D. (1999). Convolution kernels on discrete structures (Technical Report UCSC-CRL-99-10). University of California at Santa Cruz.
Hofmann, T. (1999). Probabilistic latent semantic indexing. In Proceedings of the 22nd ACM SIGIR conference on research and development in information retrieval (pp. 50–57), Berkeley, CA, USA.
Hofmann, T. (2000). Learning the similarity of documents: An information-geometric approach to document retrieval and categorization. In Advances in neural information processing systems (Vol. 12, pp. 914–920). Cambridge: MIT Press. Google Scholar
Hofmann, T. (2001). Unsupervised learning by probabilistic latent semantic analysis. Machine Learning, 42(1/2), 177–196. ArticleMATH Google Scholar
Ito, T., Shimbo, M., Kudo, T., & Matsumoto, Y. (2005). Application of kernels to link analysis. In Proceedings of the 11th ACM SIGKDD international conference on knowledge discovery and data mining (KDD’05) (pp. 586–592), Chicago, IL, USA.
Ito, T., Shimbo, M., Mochihashi, D., & Matsumoto, Y. (2006). Exploring communities with kernel-based citation analysis. In Proceedings of the 10th European conference on principles and practice of knowledge discovery in databases (PKDD’06) (pp. 235–246). Berlin: Springer. Google Scholar
Jaakkola, T., & Haussler, D. (1999). Exploiting generative models in discriminative classifiers. In Advances in neural information processing systems (Vol. 11, pp. 487–493). Cambridge: MIT Press. Google Scholar
Kandola, J., Shawe-Taylor, J., & Cristianini, N. (2002). Optimizing kernel alignment over combinations of kernels (Technical Report NC-TR-2002-121). NeuroCOLT.
Kandola, J., Shawe-Taylor, J., & Cristianini, N. (2003). Learning semantic similarity. In Advances in neural information processing systems (Vol. 15, pp. 673–680). Cambridge: MIT Press. Google Scholar
Kessler, M. M. (1963). Bibliographic coupling between scientific papers. American Documentation, 14(1), 10–25. Article Google Scholar
Kleinberg, J. M. (1999). Authoritative sources in a hyperlinked environment. Journal of the ACM, 46, 604–632. ArticleMATHMathSciNet Google Scholar
Kondor, R., & Lafferty, J. (2001). Diffusion kernels on graphs and other discrete input spaces. In Proceedings of the 18th international conference on machine learning (ICML’01) (pp. 21–24), Williamstown, MA, USA.
le Pair, C. (1988). The citation gap of applicable science. In A. F. J. van Raan (Ed.), Handbook of quantitative studies of science and technology (pp. 537–553). Amsterdam: Elsevier. Google Scholar
Lempel, R., & Moran, S. (2001). The stochastic approach for link-structure analysis. ACM Transactions on Information Systems, 19(2), 131–160. Article Google Scholar
Nadler, B., Lafon, S., Coifman, R., & Kevrekidis, I. (2006). Diffusion maps, spectral clustering and eigenfunctions of Fokker-Planck operators. In Advances in neural information processing systems (Vol. 18, pp. 955–962). Cambridge: MIT Press. Google Scholar
Saerens, M., Fouss, F., Yen, L., & Dupont, P. (2004). The principal component analysis of a graph, and its relationship to spectral clustering. In Lecture notes in artificial intelligence: Vol. 3202. Proceedings of 15th European conference on machine learning (ECML’04) (pp. 371–383), Pisa, 2004. Berlin: Springer. Google Scholar
Shawe-Taylor, J., & Cristianini, N. (2004). Kernel methods for pattern analysis. Cambridge: Cambridge University Press. Google Scholar
Shimbo, M., & Ito, T. (2006). Kernels as link analysis measures. In D. J. Cook & L. B. Holder (Eds.), Mining graph data (Chap. 12). New York: Wiley. Google Scholar
Shimbo, M., Ito, T., & Matsumoto, Y. (2007). Evaluation of kernel-based link analysis measures on research paper recommendation. In Proceedings of the ACM/IEEE joint conference on digital libraries (JCDL 2007) (pp. 354–355), Vancouver, British Columbia, Canada, 2007.
Siegel, S., & Castellan, J. N. (1988). Nonparametric statistics for the behavioral sciences (2nd ed.). Boston: McGraw Hill. Google Scholar
Small, H. (1973). Co-citation in the scientific literature: a new measure of the relationship between two documents. Journal of the American Society for Information Science, 24, 265–269. Article Google Scholar
Smola, A. J., & Kondor, R. (2003). Kernels and regularization of graphs. In Lecture notes in artificial intelligence: Vol. 2777. Proceedings of the 16th annual conference on learning theory and 7th kernel workshop (COLT/Kernel’03) (pp. 144–158). Berlin: Springer. Google Scholar
White, S., & Smyth, P. (2003). Algorithms for estimating relative importance in networks. In Proceedings of the 9th ACM SIGKDD international conference on knowledge discovery and data mining (KDD’03) (pp. 266–275), Washington, DC, USA.
Zelnik-Manor, L., & Perona, P. (2005). Self-tuning spectral clustering. In Advances in neural information processing systems (Vol. 17). Cambridge: MIT Press. Google Scholar
Zhou, D., & Schölkopf, B. (2004). A regularization framework for learning from graph data. In Proceedings of the workshop on statistical relational learning.