Learning random walks to rank nodes in graphs

ACM International Conference Proceeding Series

Volumn 227, Issue , 2007, Pages 9-16

  Agarwal, Alekh ^a   Chakrabarti, Soumen ^a  

^a INDIAN INSTITUTE OF TECHNOLOGY BOMBAY   (India)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; GRAPHIC METHODS; MARKOV PROCESSES; PROBABILITY;

PAGERANK TRANSITION PROBABILITIES; RANKING NODES; SCALABLE ALGORITHMS;

SUPPORT VECTOR MACHINES;

EID: 34547978475     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1273496.1273498     Document Type: Conference Paper

References (23)

1. Agarwal, A., Chakrabarti, S., & Aggarwal, S. (2006). Learning to rank networked entities. SIGKDD Conference (pp. 14-23).
2. Agarwal, S. (2006). Ranking on graph data. ICML (pp. 25-32).
3. Agarwal, S., Cortes, C., & Herbrich, R. (Eds.). (2005). Learning to rank, NIPS Workshop.
4. Agarwal, S., & Niyogi, P. (2005). Stability and generalization of bipartite ranking algorithms. COLT (pp. 32-47). Bertinoro.
5. Balmin, A., Hristidis, V., & Papakonstantinou, Y. (2004). Authority-based keyword queries in databases using ObjectRank. VLDB. Toronto.
6. Bousquet, O., & Elisseeff, A. (2002). Stability and generalization. Journal of Machine. Learning Research, 2, 499-526.
7. Brin, S., & Page, L. (1998). The anatomy of a large-scale hypertextual web search engine. WWW Conference.
8. Burges, C., Shaked, T., Renshaw, E., Lazier, A., Deeds, M., Hamilton, N., & Hullender, G. (2005). Learning to rank using gradient descent. ICML.
9. Burges, C. J. C., Ragno, R., & Le, Q. V. (2006). Learning to rank with nonsmooth cost functions. NIPS.
10. Chakrabarti, D., Zhan, Y., & Faloutsos, C. (2004). R-MAT: A recursive model for graph mining. ICDM.
11. Chung, F. (2005). Laplacians and the Cheeger inequality for directed graphs. Annals of Combinatorics, 9, 1-19.
12. Cover, T. M., & Thomas, J. A. (1991). Elements of information theory. John Wiley and Sons, Inc.
13. Herbrich, R., Graepel, T., & Obermayer, K. (1999). Support vector learning for ordinal regression. International Conference on Artificial Neural Networks (pp. 97-102).
14. Jeh, G., & Widom, J. (2003). Scaling personalized web search. WWW Conference (pp. 271-279).
15. Jeong, H., Tombor, B., Albert, R., Oltvai, Z. N., & Barabási, A.-L. (2000). The large-scale organization of metabolic networks. Nature, 407, 651-654.
16. Joachims, T. (2002). Optimizing search engines using clickthrough data. SIGKDD Conference. ACM.
17. Langville, A. N., & Meyer, C. D. (2004). Deeper inside PageRank. Internet Mathematics, 1, 335-380.
18. Matveeva, I., Burges, C., Burkard, T., Laucius, A., & Wong, L. (2006). High accuracy retrieval with multiple nested ranker. SIGIR Conference (pp. 437-444). Seattle, Washington, USA.
19. Rudin, C. (2006). Ranking with a p-norm push. COLT (pp. 589-604).
20. Smola, A., & Kondor, R. (2003). Kernels and regularization on graphs. COLT (pp. 144-158).
21. Taskar, B. (2004). Learning structured prediction models: A large margin approach. Doctoral dissertation, Stanford University.
22. Zhou, D., Huang, J., & Schölkopf, B. (2005). Learning from labeled and unlabeled data on a directed graph. ICML (pp. 1041-1048).
23. Zhou, D., & Schölkopf, B. (2004). A regularization framework for learning from graph data. ICML Workshop on Statistical Relational Learning.