Collective Prediction of Multiple Types of Links in Heterogeneous Information Networks

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn 2015-January, Issue January, 2014, Pages 50-59

  Cao, Bokai ^a   Kong, Xiangnan ^b   Yu, Philip S ^a  

^a University of Illinois at Chicago   (United States)

^b Worcester Polytechnic Institute   (United States)

Author keywords

collective link prediction; heterogeneous information networks; meta path

Indexed keywords

COMPLEX NETWORKS; DATA MINING; INFORMATION SERVICES;

EXISTENCE PROBABILITIES; HETEROGENEOUS INFORMATION; HETEROGENEOUS LINKS; ITERATIVE FRAMEWORK; LINK PREDICTION; META PATH; REAL-WORLD NETWORKS; RELATEDNESS MEASURES;

FORECASTING;

EID: 84936938554     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDM.2014.25     Document Type: Conference Paper

References (30)

1. L. Adamic and E. Adar. Friends and neighbors on the web. Social Networks, 25:211-230, 2001.
2. A. Blum and T. Mitchell. Combining labeled and unlabeled data with co-training. In Proceedings of the ACM Conference on Computational Learning Theory, pages 92-100, 1998.
3. C.-C. Chang and C.-J. Lin. LIBSVM: a library for support vector machines, 2001. Software available at http://www.csie.ntu.edu.tw/?cjlin/libsvm.
4. B. Chen, Y. Ding, and D. Wild. Assessing dru g target association using semantic linked data. PLOS Computational Biology, 8(7), 2012.
5. F. Cheng, C. Liu, J. Jiang, W. Lu, W. Li, G. Liu, W. Zhou, J. Huang, and Y. Tang. Prediction of drug-target interactions and drug repositioning via network-based inference. PLOS Computational Biology, 8, 2012.
6. D. Davis, R. Lichtenwalter, and N. Chawla. Multi-rel ational link prediction in heterogeneous information networks. In Proceedings of the International Conference on Advances in Social Networks Analysis and Mining, 2011.
7. L. Eronen and H. Toivonen. Biomine: predic ting links between biological entities using network models of heterogeneous databases. BMC Bioinformatics, 13(1):119, 2012.
8. L. Getoor and C. Diehl. Link mining: a survey. SIGKDD Explorations, 7:3-12, 2005.
9. A. Gottlieb, G. Stein, E. Ruppin, and R. Sharan. Predict: a method for inferring novel drug indications with application to personalized medicine. Molecular Systems Biology, 7, 2011.
10. M. Hasan, V. Chaoji, S. Salem, and M. Zaki. Link prediction using supervised learning. In SDM Workshop on Link Analysis, Counterterrorism and Security, 2006.
11. J. Hastings, P. Matos, M. Ennis, and C. Steinbeck. Towards automatic classification within the chebi ontology. Nature Precedings, 2009.
12. S. Jaeger, S. Gaudan, U. Leser, and D. R. Schuh mann. Integrating protein-protein interactions and text mining for protein function prediction. BMC Bioinformatics, 9, 2008.
13. M. Ji, J. Han, and M. Danilevsky. Ranking-based classification of heterogeneous information networks. In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 1298-1306, 2011.
14. X. Kong,B. Cao,P. S. Yu Multi-label classification by mining label and instance correlations from heterogeneous information networks In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining 2013.
15. X. Kong, P. S. Yu, Y. Ding, and D. J. Wild. Meta path-based collective classification in heterogeneous information networks. In Proceedings of the ACM International Conference on Information and Knowledge Management, 2012.
16. T. Kuo, R. Yan, Y. Huang, P. Kung, and S. Lin. Unsupervised link prediction using aggregative statistics on heterogeneous social networks. In Proceedings of the ACM SIGKDD C onference on Knowledge Discovery and Data Mining, pages 775-783, 2013.
17. N. Lao and W. Cohen. Relational retrieval using a combination of pathconstrained random walks. Machine Learning, 81(2):53-67, 2010.
18. V. Leroy, B. Cambazoglu, and F. Bonchi. Cold start link prediction. In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 393-402, 2010.
19. D. Liben-Nowell and J. Kleinberg. The link prediction problem for social networks. In Proceedings of the ACM International Conference on Information and Knowledge Management, pages 556-559, 2003.
20. R. Lichtenwlter, J. Lussier, and N. Chawla. New perspectives and methods in link prediction. In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 243-252, 2010.
21. Q. Lu and L. Getoor. Link-based classification. In Proceedings of the International Conference on Machine Learning, pages 496-503, 2003.
22. Y. Moreau and L. Tranc hevent. Computational tools for prioritizing candidate genes: boosting disease gene discovery. Nature Reviews Genetics, 13:523-536, 2012.
23. E. Pauwels, V. Stoven, and Y. Yamanishi. Pre dicting drug side-effect profiles: a chemical fragment-based approach. BMC Bioinformatics, 12, 2011.
24. C. Shi, X. Kong, P. S. Yu, S. Xie, and B. Wu. Relevance search in heterogeneous networks. In Proceedings of the International Conference on Extending Database Technology, pages 180-191. ACM, 2012.
25. Y. Sun, R. Barber, M. Gupta, C. Aggarwal, and J. Han. Co-Author relationship prediction in heterogeneous bibliographic networks. In Proceedings of the International Conference on Advances in Social Networks Analysis and Mining, 2011.
26. Y. Sun, J. Han, X. Yan, P. Yu, and T. Wu. PathSim: Meta pathbased top-k similarity search in heterogeneous information networks. In Proceedings of the International Conference on Very Large Data Bases, 2011.
27. Y. Sun, B. Norick, J. Han, X. Yan, P. S. Yu, and X. Yu. Integrating meta-path selection with user-guided object clustering in heterogeneous information networks. In Proceedings of the ACM SIGKDD Confe rence on Knowledge Discovery and Data Mining, pages 1348-1356, 2012.
28. Y. Sun, Y. Yu, and J. Han. Ranking-based clustering of heterogeneous information networks with star network schema. In Proce edings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 797-806, 2009.
29. A. Vinayagam, C. Val, F. Schubert, R. Eils, K. Glatting, S. Suhai, and R. Konig. GOPET: A tool for automated predictions of gene ontology terms. BMC Bioinformatics, 7, 2006.
30. C. Wang, V. Satuluri, and S. Parthasarathy. Local probabilistic models for link prediction. In Proceedings of the IEEE International Conference on Data Mining, pages 322-331, 2007.