Uncovering the temporal dynamics of diffusion networks

Proceedings of the 28th International Conference on Machine Learning, ICML 2011

Volumn , Issue , 2011, Pages 561-568

  Gomez Rodriguez, Manuel ^a,b   Balduzzi, David ^a   Schölkopf, Bernhard ^a  

^a MAX PLANCK INSTITUTE FOR INTELLIGENT SYSTEMS   (Germany)

^b STANFORD UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DIFFUSION NETWORKS; DIFFUSION PROCESS; DISCRETE NETWORKS; GLOBAL DIFFUSION; OBSERVED DATA; OPTIMIZATION PROBLEMS; PARAMETER-TUNING; SPARSE SOLUTIONS; SYNTHETIC DATA; TEMPORAL DYNAMICS; TRANSMISSION RATES; TRANSMISSION SOURCES; UNDERLYING DYNAMICS;

DYNAMICS; LEARNING SYSTEMS;

DIFFUSION;

EID: 80053444447     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (16)

1. Adar, E. and Adamic, L. A. Tracking information epidemics in blogspace. In Web Intelligence, pp. 207-214, 2005.
2. Barabási, A.-L. and Albert, R. Emergence of scaling in random networks. Science, 286:509-512, 1999.
3. Boyd, S.P. and vandenberghe, L. Convex optimization. Cambridge University Press, 2004.
4. Clauset, A., Moore, C., and Newman, M. E. J. Hierarchical structure and the prediction of missing links in networks. Nature, 453(7191):98-101, 2008. (Pubitemid 351630323)
5. Erdocombining double acute accents, P. and Rényi, A. On the evolution of random graphs. Publication of the Mathematical Institute of the Hungarian Academy of Science, 5:17-67, 1960.
6. Gomez-Rodriguez, M., Leskovec, J., and Krause, A. Inferring Networks of Diffusion and Influence. In Proc. of the 16th ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, pp. 1019-1028, 2010.
7. Grant, M. and Boyd, S. CVX: Matlab software for disciplined convex programming, version 1.21. http://cvxr.com/cvx, 2010.
8. Kempe, D., Kleinberg, J. M., and Tardos, É. Maximizing the spread of influence through a social network. In Proc. of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 137-146, 2003.
9. Lappas, T., Terzi, E., Gunopulos, D., and Mannila, H. Finding effectors in social networks. In Proc. of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1059-1068, 2010.
10. Lawless, J.F. Statistical models and methods for lifetime data. Wiley New York, 1982.
11. Leskovec, J., Lang, K. J., Dasgupta, A., and Mahoney, M. W. Statistical properties of community structure in large social and information networks. In Proc. of the 17th International Conference on World Wide Web, 2008.
12. Leskovec, J., Chakrabarti, D., Kleinberg, J., Faloutsos, C., and Ghahramani, Z. Kronecker graphs: An approach to modeling networks. The Journal of Machine Learning Research, 11:985-1042, 2010.
13. Meyers, S. and Leskovec, J. On the Convexity of Latent Social Network Inference. In Advances in Neural Information Processing Systems, 2010.
14. Newey, W. K. and McFadden, D. L. Large sample estimation and hypothesis testing. In Handbook of Econometrics, Volume IV, pp. 2111-2245. 1994.
15. Wallinga, J. and Teunis, P. Different epidemic curves for severe acute respiratory syndrome reveal similar impacts of control measures. American Journal of Epidemiology, 160(6):509-516, 2004. (Pubitemid 39215626)
16. Watts, Duncan J. and Dodds, Peter S. Influentials, networks, and public opinion formation. Journal of Consumer Research, 34(4):441-458, 2007.