Label-dependent node classification in the network

Neurocomputing

Volumn 75, Issue 1, 2012, Pages 199-209

  Kazienko, Przemyslaw ^a   Kajdanowicz, Tomasz ^a  

^a WROCLAW UNIVERSITY OF TECHNOLOGY   (Poland)

Author keywords

Bootstrapping; Classification; Classification in networks; Collective classification; Gibbs sampling; Label dependent classification; Label dependent features; LDBootstrapping; LDGibbs; Node classification

Indexed keywords

HYPERTEXT SYSTEMS; WEBSITES;

BOOTSTRAPPING; CLASSIFICATION IN NETWORKS; COLLECTIVE CLASSIFICATIONS; GIBBS SAMPLING; LDBOOTSTRAPPING; LDGIBBS;

CLASSIFICATION (OF INFORMATION);

ACCURACY; ARTICLE; BOOTSTRAPPING; CLASSIFICATION ALGORITHM; COMPUTER NETWORK; COMPUTER PREDICTION; COMPUTER SYSTEM; GIBBS SAMPLING; IMAGE PROCESSING; INTERMETHOD COMPARISON; MATHEMATICAL COMPUTING; PRIORITY JOURNAL; PROCESS DEVELOPMENT; SOCIAL NETWORK; STATISTICAL ANALYSIS; STRUCTURAL BIOINFORMATICS; STRUCTURE ANALYSIS;

EID: 82455198660     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2011.04.047     Document Type: Article

References (36)

1. Abraham A., Corchado E., Corchado J. Hybrid learning machines. Neurocomputing 2009, 72:2729-2730.
2. Chakrabarti S., Dom B., Indyk P. Enhanced hypertext categorization using hyperlinks. The Proceedings of the International Conference on Management of Data SIGMOD Conference 1998, 307-318.
3. Corchado E., Abraham A., de Carvalho A.P.L.F. Hybrid intelligent algorithms and applications. Inf. Sci. 2010, 180:2633-2634.
4. C. Desrosiers, G. Karypis, Within-network classification using local structure similarity, in: Lecture Notes in Computer Science, vol. 5781, 2009, pp. 260-275.
5. Friedman N., Getoor L., Koller D., Pfeffer A. Learning probabilistic relational models. Proceedings of The International Joint Conference on Artificial Intelligence IJCAI-99 1999, 1300-1309.
6. Gallagher B., Eliassi-Rad T. Leveraging label-independent features for classification in sparsely labeled networks: an empirical study. Proceedings of the Second ACM SIGKDD Workshop on Social Network Mining and Analysis, SNA-KDD'08 2008.
7. B. Gallagher, T. Eliassi-Rad, Leveraging Network Structure to Infer Missing Values in Relational Data, Lawrence Livermore National Laboratory Technical Report UCRL-TR-231993, 2007.
8. Gupta R., Diwan A.A., Sarawagi S. Efficient inference with cardinality-based clique potentials. Proceedings of the 24th International Conference on Machine Learning 2007, 329-336.
9. Geman S., Geman D. Stochastic relaxation Gibbs distributions and the Bayesian restoration of images. IEEE Trans. Pattern Anal. Mach. Intell. 1984, 6:721-741.
10. Getoor L. Link-based classification. Advanced Methods for Knowledge Discovery from Complex Data 2005, 184-207. Springer-Verlag.
11. Jung J. An evolutionary approach to query sampling for heterogeneous systems. Expert Syst. Appl. 2010, 37:226-232.
12. Jensen D., Neville J., Gallagher B. Why collective inference improves relational classification. The Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2004, 593-598.
13. Jensen D., Neville J. Linkage and autocorrelation cause feature selection bias in relational learning. Proceedings of the 19th International Conference on Machine Learning 2002, 259-266.
14. Kajdanowicz T., Kazienko P. A method of label-dependent feature extraction in social networks. ICCCI'2010, The Second International Conference on Computational Collective Intelligence Technology and Applications, Lecture Notes in Artificial Intelligence 2010, vol. 6422:11-21.
15. Kajdanowicz T., Kazienko P., Doskocz P. Label-dependent feature extraction in social networks for node classification. The Proceedings of Second International Conference on Social Informatics, Lecture Notes in Artificial Intelligence 2010, vol. 6430:89-102.
16. Kajdanowicz T., Kazienko P., Doskocz P., Litwin K. An assessment of node classification accuracy in social networks using label-dependent feature extraction. The Third World Summit on the Knowledge Society WSKS 2010, Communications in Computer and Information Science 2010, vol. 111:125-130.
17. Kamada T., Kawai S. An algorithm for drawing general undirected graphs. Inf. Process. Lett. 1989, 31:7-15.
18. Knobbe A., deHaas M., Siebes A. Propositionalisation and aggregates. Proceedings of the Fifth European Conference on Principles of Data Mining and Knowledge Discovery 2001, 277-288.
19. Kramer S., Lavrac N., Flach P. Propositionalization approaches to relational data mining. Relational Data Mining 2001, 262-286. Springer-Verlag. S. Dezeroski (Ed.).
20. Krogel M., Rawles S., Zeezny F., Flach P., Lavrac N., Wrobel S. Comparative evaluation of approaches to propositionalization. The Proceedings of 13th International Conference on Inductive Logic Programming 2003, 180-196.
21. Lu Q., Getoor L. Link-based classification. Proceedings of the 20th International Conference on Machine Learning ICML 2003, 496-503.
22. Macskassy S., Provost F. Classification in networked data: a toolkit and a univariate case study. J. Mach. Learn. Res. 2007, 8:935-983.
23. Macskassy S., Provost F. A simple relational classifier. The Proceedings of KDD-2003 Workshop on Multi-Relational Data Mining MRDM-2003 2003, 64-76.
24. McDowell L.K., Gupta K.M., Aha D.W. Cautious inference in collective classification. The Proceedings of the Twenty-Second AAAI Conference on Artificial Intelligence 2007, 596-601.
25. McDowell L., Gupta K., Aha D. Case-based collective classification. Proceedings of the Twentieth International FLAIRS Conference 2007, AAAI Press.
26. Musial K., Kazienko P., Brodka P. User position measures in social networks. The Third SNA-KDD Workshop on Social Network Mining and Analysis held in Conjunction with the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining KDD 2009, ACM Press, Article no. 6.
27. Neville J., Jensen D. Iterative classification in relational data. Proceedings of the AAAI 2000 Workshop Learning Statistical Models from Relational Data 2000, 42-49. AAAI Press.
28. Neville J., Jensen D. Collective classification with relational dependency networks. Proceedings of the Second International Workshop on Multi-Relational Data Mining 2003, 77-91.
29. Pearl J. Probabilistic Reasoning in Intelligent Systems 1988, Morgan Kaufmann.
30. C. Perlich, F. Provost, Aggregation-based feature invention and relational concept classes, in: ACM SIGKDD in the Proceedings of Ninth International Conference on Knowledge Discovery and Data Mining, 2003, pp. 167-176.
31. Perlich C., Provost F. Distribution-based aggregation for relational learning with identifier attributes. Mach. Learn. 2006, 62(1-2):65-105.
32. Richardson M., Domingos P. Markov logic networks. Mach. Learn. 2006, 62(1-2):107-136.
33. Sen P., Namata G., Bilgic M., Getoor L., Gallagher B., Eliassi-Rad T. Collective classification in network data. Artif. Intell. Mag. 2008, 29(3):93-106.
34. Sen P., Getoor L. Empirical comparison of approximate inference algorithms for networked data. ICML Workshop on Open Problems in Statistical Relational Learning 2006.
35. Taskar B., Abbeel P., Koller D. Discriminative probabilistic models for relational data. Proceedings of the Eighteenth Conference on Uncertainty in Artificial Intelligence 2002, 485-492.
36. Wasserman S., Faust K. Social Network Analysis: Methods and Applications 1994, Cambridge University Press, New York.