Generative models of information diffusion with asynchronous time-delay

Journal of Machine Learning Research

Volumn 13, Issue , 2010, Pages 193-208

  Saito, Kazumi ^a   Kimura, Masahiro ^b   Ohara, Kouzou ^c   Motoda, Hiroshi ^d  

^a UNIVERSITY OF SHIZUOKA   (Japan)

^b RYUKOKU UNIVERSITY   (Japan)

^c AOYAMA GAKUIN UNIVERSITY   (Japan)

^d OSAKA UNIVERSITY   (Japan)

Author keywords

Asynchronous time delay; Information diffusion; Maximum likelihood; Social network

Indexed keywords

INFORMATION DIFFUSION; INFORMATION DIFFUSION PROCESS; INFORMATION PROPAGATION; LEARNING FRAMEWORKS; LIKELIHOOD FUNCTIONS; REAL-WORLD NETWORKS; SOCIAL NETWORKS; SPREAD OF INFORMATIONS;

COMPUTER SIMULATION; LEARNING SYSTEMS; MAXIMUM LIKELIHOOD; SOCIAL NETWORKING (ONLINE);

TIME DELAY;

EID: 84875854513     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Conference Paper

References (16)

1. P. Domingos. Mining social networks for viral marketing. IEEE Intelligent Systems, 20: 80-82, 2005.
2. J. Goldenberg, B. Libai, and E. Muller. Talk of the network: A complex systems look at the underlying process of word-of-mouth. Marketing Letters, 12:211-223, 2001.
3. D. Gruhl, R. Guha, D. Liben-Nowell, and A. Tomkins. Information diffusion through blogspace. SIGKDD Explorations, 6:43-52, 2004.
4. D. Kempe, J. Kleinberg, and E. Tardos. Maximizing the spread of influence through a social network. In Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2003), pages 137-146, 2003.
5. M. Kimura, K. Saito, and H. Motoda. Blocking links to minimize contamination spread in a social network. ACM Transactions on Knowledge Discovery from Data, 3:9:1-9:23, 2009.
6. M. Kimura, K. Saito, R. Nakano, and H. Motoda. Extracting influential nodes on a social network for information diffusion. Data Mining and Knowledge Discovery, Springer, 20:70-97, 2010.
7. B. Klimt and Y. Yang. The enron corpus: A new dataset for email classification research. In Proceedings of the 2004 European Conference on Machine Learning (ECML'04), pages 217-226, 2004.
8. J. Leskovec, L. A. Adamic, and B. A. Huberman. The dynamics of viral marketing. In Proceedings of the 7th ACM Conference on Electronic Commerce (EC'06), pages 228- 237, 2006.
9. M. E. J. Newman. The structure and function of complex networks. SIAM Review, 45: 167-256, 2003.
10. M. E. J. Newman, S. Forrest, and J. Balthrop. Email networks and the spread of computer viruses. Physical Review E, 66:035101, 2002.
11. G. Palla, I. Deŕenyi, I. Farkas, and T. Vicsek. Uncovering the overlapping community structure of complex networks in nature and society. Nature, 435:814-818, 2005.
12. K. Saito, M. Kimura, K. Ohara, and H. Motoda. Learning continuous-time information diffusion model for social behavioral data analysis. In Proceedings of the 1st Asian Conference on Machine Learning (ACML2009), pages 322-337, 2009.
13. K. Saito, M. Kimura, K. Ohara, and H. Motoda. Behavioral analyses of information diffusion models by observed data of social network. In Proceedings of the International Workshop on Social Computing and Behavioral Modeling (SBP10), pages 149-158, 2010.
14. K. Saito, R. Nakano, and M. Kimura. Prediction of information diffusion probabilities for independent cascade model. In Proceedings of the 12th International Conference, Knowledge-based Intelligent Information and Engineering Systems (KES2008), pages 41- 49. LNAI 5179, 2008.
15. D. J.Watts. A simple model of global cascades on random networks. Proceedings of National Academy of Science, USA, 99:5766-5771, 2002.
16. D. J. Watts and P. S. Dodds. Influence, networks, and public opinion formation. Journal of Consumer Research, 34:441-458, 2007.