Collective prediction with latent graphs

International Conference on Information and Knowledge Management, Proceedings

Volumn , Issue , 2011, Pages 1127-1136

  Shi, Xiaoxiao ^a   Li, Yao ^a   Yu, Philip ^a  

^a 1120 Science and Engineering Offices   (United States)

Author keywords

collective classification; latent graph

Indexed keywords

CLASSIFICATION MODELS; COLLABORATION NETWORK; COMPARISON MODELS; DOCUMENT CLASSIFICATION; LABEL PROPAGATION; LABELED DATA; LATENT GRAPH; LEARNING ACCURACY; LOCAL CONNECTIONS; NETWORK INTRUSION DETECTION; OPTIMIZATION PROBLEMS; REAL-WORLD APPLICATION; RELATIONAL DATA; RESEARCH TOPICS; SOCIAL NETWORK ANALYSIS; TRAINING DATA;

INTRUSION DETECTION; KNOWLEDGE MANAGEMENT; SOCIAL NETWORKING (ONLINE);

INFORMATION RETRIEVAL SYSTEMS;

EID: 83055186952     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2063576.2063739     Document Type: Conference Paper

References (24)

1. T. F. Coleman and Y. Li. A reflective newton method for minimizing a quadratic function subject to bounds on some of the variables. SIAM Journal on Optimization, 6(4):1040-1058, 1996.
2. J. Demmel, I. Dumitriu, O. Holtz, and R. Kleinberg. Fast matrix multiplication is stable. CoRR, abs/math/0603207, 2006.
3. B. Gallagher and T. Eliassi-Rad. Leveraging network structure to infer missing values in relational data. Technical Report UCRL-TR-231993.
4. L. Getoor. Advanced Methods for Knowledge Discovery from Complex Data. Springer.
5. W. R. Grilks, S. Richardson, and D. J. Spiegelhalter. Markov chain monte carlo in practice. Interdisciplinary Statistics. Chapman and Hall/CRC, 1996.
6. D. Jensen, J. Neville, and B. Gallagher. Why collective inference improves relational classification. In Proceedings of the International Conference on Knowledge Discovery and Data Mining, 2004.
7. J. D. Lafferty, A. McCallum, and F. C. N. Pereira. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In Proceedings of the International Conference on Machine Learning, 2001.
8. J. Leskovec, L. A. Adamic, and B. A. Huberman. The dynamics of viral marketing. ACM Transactions on the Web (TWEB), 1:1559-1131, 2007.
9. Q. Lu and L. Getoor. Link-based classification. In Proceedings of the Twentieth International Conference on Machine Learning, 2003.
10. S. A. Macskassy and F. Provost. A simple relational classifier. In Proceedings of the Multi-Relational Data MiningWorkshop at the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2003.
11. A. McCallum, K. Nigam, J. Rennie, and K. Seymore. Automating the construction of internet portals with maching learning. Information Retrieval, 2(3):127-163, 2000.
12. J. Neville and D. Jensen. Iterative classification in relational data. In Proceeding of the Workshop on Statistical Relational Learning of the National Conference on Artificial Intelligence, 2000.
13. J. Neville and D. Jensen. Collective classification with relational dependency networks. In Proceedings of the 2nd Multi-Relational Data Mining Workshop, ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2003.
14. P. Sen, G. M. Namata, M. Bilgic, L. Getoor, B. Gallagher, and T. Eliassi-Rad. Collective classification in network data. AI Magazine, 29(3):93-106, 2008.
15. B. Taskar, P. Abbeel, and D. Koller. Discriminative probabilistic models for relational data. In Proceedings of the Annual Conference on Uncertainty in Artificial Intelligence, 2002.
16. B. Taskar, V. Chatalbashev, D. Koller, and C. Guestrin. Learning structured prediction models: A large margin approach. In Proceedings of the International Conference on Machine Learning, 2005.
17. B. Taskar, M. F. Wong, P. Abbeel, and D. Koller. Link prediction in relational data. In Neural Information Processing Systems, 2003.
18. Y. Weiss. Advanced Mean Field Methods. MIT Press.
19. Jonathan S. Yedidia, William T. Freeman, and Yair Weiss. Constructing free energy approximations and generalized belief propagation algorithms. IEEE Transactions on Information Theory, 51:2282-2312, 2004.
20. D. Zhou, O. Bousquet, T. N. Lal, J. Weston, and B. Schaolkopf. Learning with local and global consistency. In Neural Information Processing Systems, 2004.
21. D. Zhou and B. Scholkopf. Learning from labeled and unlabeled data using random walks. In Proceedings of the DAGM Symposium, 2004.
22. Y. Zhou, H. Cheng, and J. X. Yu. Graph clustering based on structural/attribute similarities. In Proceedings of the Very Large Data Base, 2009.
23. X. Zhu. Semi-supervised learning with graphs. Technical report, Carnegie Mellon University, 2005.
24. X. Zhu, Z. Ghahramani, and J. Lafferty. Semi-supervised learning using gaussian fields and harmonic functions. In Proceedings of the International Conference on Machine Learning, 2003.