Leveraging label-independent features for classification in sparsely labeled networks: An empirical study

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5498 LNAI, Issue , 2009, Pages 1-19

  Gallagher, Brian ^a   Eliassi Rad, Tina ^a  

^a LAWRENCE LIVERMORE NATIONAL LABORATORY   (United States)

Author keywords

Collective classification; Feature extraction; Semi supervised learning; Social network analysis; Statistical relational learning

Indexed keywords

CLASS LABELS; CLASSIFICATION PERFORMANCE; COLLECTIVE CLASSIFICATION; EMPIRICAL STUDIES; NETWORK CLASSIFICATION; NETWORK-BASED; PERFORMANCE IMPROVEMENTS; REAL WORLD DATA; RELATIVE UTILITY; SEMI-SUPERVISED LEARNING; SOCIAL NETWORK ANALYSIS; SPECIFIC TASKS; STATISTICAL RELATIONAL LEARNING; STRUCTURAL CHARACTERISTICS;

ARTIFICIAL INTELLIGENCE; CLASSIFIERS; ELECTRIC NETWORK ANALYSIS; FEATURE EXTRACTION; SUPERVISED LEARNING;

CLASSIFICATION (OF INFORMATION);

EID: 78650094757     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-14929-0_1     Document Type: Conference Paper

References (25)

1. Taskar, B., Abbeel, P., Koller, D.: Discriminative probabilistic models for relational data. In: Proceedings of the 18th Conference on Uncertainty in AI, pp. 485-492 (2002)
2. Sen, P., Namata, G., Bilgic, M., Getoor, L., Gallagher, B., Eliassi-Rad, T.: Collective classification in network data. AI Magazine 29(3), 93-106 (2008)
3. Neville, J., Jensen, D.: Relational dependency networks. Journal of Machine Learning Research 8, 653-692 (2007)
4. Getoor, L., Friedman, N., Koller, D., Taskar, B.: Learning probabilistic models of link structure. Journal of Machine Learning Research 3, 679-707 (2002)
5. Lu, Q., Getoor, L.: Link-based classification. In: Proceedings of the 20th International Conference on Machine Learning, pp. 496-503 (2003)
6. Neville, J., Jensen, D., Gallagher, B.: Simple estimators for relational bayesian classifiers. In: Proceedings the 3rd IEEE International Conference on Data Mining, pp. 609-612 (2003)
7. Macskassy, S., Provost, F.: Classification in networked data: A toolkit and a univariate case study. Journal of Machine Learning Research 8, 935-983 (2007)
8. Neville, J., Jensen, D., Friedland, L., Hay, M.: Learning relational probability trees. In: Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 625-630 (2003)
9. Perlich, C., Provost, F.: Aggregation-based feature invention and relational concept classes. In: Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 167-176 (2003)
10. Singh, L., Getoor, L., Licamele, L.: Pruning social networks using structural properties and descriptive attributes. In: Proceedings of the 5th IEEE International Conference on Data Mining, pp. 773-776 (2005)
11. Neville, J., Jensen, D.: Leveraging relational autocorrelation with latent group models. In: Proceedings the 5th IEEE International Conference on Data Mining, pp. 322-329 (2005)
12. Chakrabarti, S., Dom, B., Indyk, P.: Enhanced hypertext categorization using hyperlinks. In: Proceedings of ACM SIGMOD International Conference on Management of Data, pp. 307-318 (1998)
13. Jensen, D., Neville, J., Gallagher, B.: Why collective inference improves relational classification. In: Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 593-598 (2004)
14. McDowell, L., Gupta, K., Aha, D.: Cautious inference in collective classification. In: Proceedings of the 22nd AAAI Conference on Artificial Intelligence, pp. 596-601 (2007)
15. Zhu, X., Ghahramani, Z., Lafferty, J.: Semi-supervised learning using gaussian fields and harmonic functions. In: Proceedings of the 20th International Conference on Machine Learning, pp. 912-919 (2003)
16. Zhu, X.: Semi-supervised learning literature survey. Technical Report CS-TR-1530, University of Wisconsin, Madison, WI (December 2007), http://pages.cs.wisc.edu/~jerryzhu/pub/ssl-survey.pdf
17. Gallagher, B., Tong, H., Eliassi-Rad, T., Faloutsos, C.: Using ghost edges for classification in sparsely labeled networks. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 256-264 (2008)
18. Newman, M.: The structure and function of complex networks. SIAM Review 45, 167-256 (2003)
19. Macskassy, S., Provost, F.: A simple relational classifier. In: Notes of the 2nd Workshop on Multi-relational Data Mining at KDD 2003 (2003)
20. Gallagher, B., Eliassi-Rad, T.: An examination of experimental methodology for classifiers of relational data. In: Proceedngs of the 7th IEEE International Conference on Data Mining Workshops, pp. 411-416 (2007)
21. Krebs, V.: Books about U.S. politics (2004), http://www.orgnet.com/ divided2.html
22. Cohen, W.: Enron email data set, http://www.cs.cmu.edu/~enron/
23. Eagle, N., Pentland, A.: Reality mining: sensing complex social systems. Journal of Personal and Ubiquitous Computing 10(4), 255-268 (2006), http://reality.media.mit.edu
24. Jensen, D.: Proximity HEP-TH database, http://kdl.cs.umass.edu/data/ hepth/hepth-info.html
25. Breiman, L.: Random forests. Machine Learning 45(1), 5-32 (2001)