Predicting links in multi-relational and heterogeneous networks

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2012, Pages 755-764

  Yang, Yang ^a   Chawla, Nitesh ^a   Sun, Yizhou ^b   Hani, Jiawei ^b  

^a University of Notre Dame   (United States)

^b University of Illinois at Urbana Champaign   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COAUTHORSHIP; DEPENDENCY STRUCTURES; HOMOGENEOUS NETWORK; IN-NETWORK ANALYSIS; LINK PREDICTION; PROBABILISTIC METHODS; SOCIAL NETWORKS; SPARSE NETWORK; TEMPORAL INFORMATION;

COMPLEX NETWORKS; DATA MINING; HETEROGENEOUS NETWORKS;

FORECASTING;

EID: 84874090252     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDM.2012.144     Document Type: Conference Paper

References (28)

1. R. Lichtenwalter, J. Lussier, and N. Chawla, New perspectives and methods in link prediction, in Proceedings of the 16th ACM SIGKDD, 2010, pp. 243-252.
2. D. Davis, R. Lichtenwalter, and N. V. Chawla, Multi-Relational Link Prediction in Heterogeneous Information Networks, ASONAM, Kaohsiung, Taiwan, 2011.
3. Y. Sun, R. Barbery, M. Gupta, C. C. Aggarwal, J. Han, Co-Author Relationship Prediction in Heterogeneous Bibliographic Networks, ASONAM, Kaohsiung, Taiwan, 2011.
4. D. Kempe, J. M. Kleinberg, and E. Tardos. Maximizing the spread of influence through a social network. In Proceedings of the 9th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 137146, 2003.
5. I. H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann, second edition, 2005.
6. L. Adamic and E. Adar, Friends and neighbors on the web. Social Networks, 25:211-230, 2001.
7. A.-L. Barabasi, H. Jeong, Z. Neda, E. Ravasz, A. Schubert, and T. Vicsek. Evolution of the social network of scientific collaboration, Physica A, 311(3-4):590-614, 2002.
8. M. E. J. Newman, Clustering and preferential attachment in growing networks, Physical Review Letters E, 64, 2001.
9. L. Katz, A new status index derived from sociometric analysis, Psychometrika, 18(1):39-43, 1953.
10. D. Liben-Nowell and J. Kleinberg. The link-prediction problem for social networks. Journal of the American Society for Information Science and Technology, 58(7):10191031, 2007.
11. L. Breiman. Random forests. Machine Learning, 45(1):532, 2001.
12. le Cessie, S. and van Houwelingen, J.C. (1997). Ridge Estimators in Logistic Regression. Applied Statistics, Vol. 41, No. 1, pp. 191-201.
13. B. Taskar, M. fai Wong, P. Abbeel, and D. Koller. Link prediction in relational data. In NIP '03, 2003.
14. M. Mitzenmacher, A brief history of lognormal and power law distributions, In Proceedings of the Allerton Conference on Communication, Control, and Computing, 2001.
15. H. Deng, J. Han, B. Zhao, Y. Yu and C. Xide Lin. Probabilistic topic models with biased propagation on heterogeneous information networks, KDD 2011, pp.1271-1279.
16. Dickey, D. A. and Fuller, W.A. Distribution of the estimated for autoregressive time series with a unit root, Journal of the American Statistical Association 74:427-431, 1979.
17. Potgieter, A.; April, K.; Cooke, R.; and Osunmakinde, I. Temporality in link prediction: Understanding social complexity, Sprouts: Working Papers on Info. Sys. 2007.
18. Huang, Z., and Lin, D., The time-series link prediction problem with applications in communication surveillance, INFORMS Journal on Computing, 2009.
19. A. Popescul, R. Popescul, and L. H. Ungar. Statistical relational learning for link prediction. In Proc. of the Workshop on Learning Statistical Models from Relational Data at IJCAI-2003., 2003.
20. R. N. Lichtenwalter and N. V. Chawla. Vertex collocation profiles: subgraph counting for link analysis and prediction in Proceedings of the 21st international conference on World Wide Web, Lyon, France, 2012.
21. S. Scellato, A. Noulas, and C. Mascolo. Exploiting place features in link prediction on location-based social networks. In Proc. of the ACM SIGKDD Intl. Conf. on Knowledge Discovery and Data Mining, 2011.
22. R. Singh, J. Xu, and B. Berger. (2008) Global alignment of multiple protein interaction networks with application to functional orthology detection, Proc. Natl. Acad. Sci. USA.
23. C. Liao, K. Lu, M. Baym, R. Singh, and B. Berger. (2009) IsoRankN: Spectral methods for global alignment of multiple protein networks, Bioinformatics.
24. L. Breiman. Random forests. Machine Learning, 2001.
25. G. Rossetti, M. Berlingerio, and F. Giannotti. Scalable link prediction on multidimensional networks via structural analysis. In ICDM Workshop, 2011.
26. L. Tang and H. Liu. Relational learning via latent social dimensions. In KDD '09: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, New York, NY, USA, 2009
27. L. Tang and H. Liu. Uncovering cross-dimension group structures in multi-dimensional networks. In SDM workshop on Analysis of Dynamic Networks, 2009.
28. M. Berlingerio, M. Coscia, F. Giannotti, A. Monreale, D. Pedreschi: Foundations of Multidimensional Network Analysis. ASONAM 2011: 485-489