A matrix alignment approach for link prediction

Proceedings - International Conference on Pattern Recognition

Volumn , Issue , 2008, Pages

  Scripps, Jerry ^a   Tan, Pang Ning ^a   Chen, Feilong ^a   Esfahanian, Abdol Hossein ^a  

^a Michigan State University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

SOFTWARE ENGINEERING;

ADJACENCY MATRICES; DISCRIMINATIVE LEARNING; LINK PREDICTION; LINK STRUCTURE; NETWORK DATA; NODE ATTRIBUTE; TOPOLOGICAL FEATURES;

PATTERN RECOGNITION;

EID: 77957962058     PISSN: 10514651     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/icpr.2008.4761444     Document Type: Conference Paper

References (8)

1. R. Duda, P. Hart, and D. Stork. Pattern Classification. John Wiley & Sons, New York, 2000.
2. L. Getoor and C. P. Diehl. Link mining: a survey. SIGKDD Explorations, 2005.
3. M. Al Hasan, V. Chaoji, S. Salem, and M. Zaki. Link prediction using supervised learning. SDM'06: Workshop on Link Analysis, Counter-terrorism and Security, 2006.
4. H. Kashima and N. Abe. A parameterized probabilistic model of network evolution for supervised link prediction. ICDM, 2006.
5. D. Liben-Nowell and J. Kleinberg. The link prediction problem for social networks. CIKM, 2003.
6. J. Neville and D. Jensen. Leveraging relational autocorrelation with latent group models. ICDM, 2005.
7. A. Ng and M. Jordan. On discriminative vs. generative classifiers: A comparison of logistic regression and naive bayes. Advances in Neural Information Processing Systems, 2002.
8. B. Taskar, P. Abbeel, and D. Koller. Discriminative probabilistic models for relational data. UAI02, 2002.