Influence maximization in continuous time diffusion networks

Proceedings of the 29th International Conference on Machine Learning, ICML 2012

Volumn 1, Issue , 2012, Pages 313-320

  Gomez Rodriguez, Manuel ^a   Schölkopf, Bernhard ^b  

^a MAX PLANCK INSTITUTE FOR INTELLIGENT SYSTEMS   (Germany)

^b STANFORD UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CONTINUOUS TIME; CONTINUOUS TIME MARKOV CHAIN; DIFFUSION NETWORKS; DIFFUSION PROCESS; EFFICIENT APPROXIMATION ALGORITHMS; INFLUENCE MAXIMIZATIONS; NEAR-OPTIMAL PERFORMANCE; NETWORK TOPOLOGY; NP-HARD; OPTIMAL SETS; SOURCE NODES; STATE-OF-THE-ART ALGORITHMS; TEMPORAL DYNAMICS;

APPROXIMATION ALGORITHMS; ELECTRIC NETWORK TOPOLOGY; LEARNING SYSTEMS; MARKOV PROCESSES; OPTIMIZATION;

CONTINUOUS TIME SYSTEMS;

EID: 84867134180     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (22)

1. Barabási, A.-L. and Albert, R. Emergence of scaling in random networks. Science, 286:509-512, 1999.
2. Bharathi, S., Kempe, D., and Salek, M. Competitive influence maximization in social networks. Internet and Network Economics, pp. 306-311, 2007.
3. Chen, W., Wang, Y., and Yang, S. Efficient influence maximization in social networks. In KDD, 2009.
4. Chen, W., Wang, C., and Wang, Y. Scalable influence maximization for prevalent viral marketing in large-scale social networks. In KDD, 2010.
5. 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.
6. Erdo{combining double acute accent}s, 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.
7. Georgiadis, L., Werneck, R.F., Tarjan, R.E., et al. Finding dominators in practice. Journal of Graph Algorithms and Applications, 10(1):69-94, 2006.
8. Gikhman, I.I. and Skorokhod, A. V. The theory of stochastic processes, volume 2. Springer Verlag, 2004.
9. Gomez-Rodriguez, M. and Schölkopf, B. Submodular Inference of Diffusion Networks from Multiple Trees. In ICML, 2012.
10. Gomez-Rodriguez, M., Leskovec, J., and Krause, A. Inferring Networks of Diffusion and Influence. In KDD, 2010.
11. Gomez-Rodriguez, M., Balduzzi, D., and Schölkopf, B. Uncovering the Temporal Dynamics of Diffusion Networks. In ICML, 2011.
12. Goyal, A., Bonchi, F., Lakshmanan, L.V.S., et al. Approximation Analysis of Influence Spread in Social Networks. Arxiv preprint arXiv: 1008.2005, 2010.
13. Kempe, D., Kleinberg, J. M., and Tardos, E. Maximizing the spread of influence through a social network. In KDD, 2003.
14. Kulkarni, V.G. Shortest paths in networks with exponentially distributed arc lengths. Networks, 16(3):255-274, 1986.
15. Leskovec, J., Krause, A., Guestrin, C., et al. Cost-effective outbreak detection in networks. In KDD, 2007.
16. Leskovec, J., Backstrom, L., and Kleinberg, J. Memetracking and the dynamics of the news cycle. In KDD, 2009.
17. Leskovec, J., Chakrabarti, D., Kleinberg, J., et al. Kronecker graphs: An approach to modeling networks. JMLR, 11:985-1042, 2010.
18. Mathioudakis, M., Bonchi, F., Castillo, C., et al. Sparsification of influence networks. In KDD, 2011.
19. Nemhauser, GL, Wolsey, LA, and Fisher, ML. An analysis of approximations for maximizing submodular set functions. Mathematical Programming, 14(1), 1978.
20. Provan, J.S. and Shier, D.R. A paradigm for listing (s, t)-cuts in graphs. Algorithmica, 15(4):351-372, 1996.
21. Richardson, M. and Domingos, P. Mining knowledge-sharing sites for viral marketing. In KDD, 2002.
22. 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.