Change point detection for burst analysis from an observed information diffusion sequence of tweets

Journal of Intelligent Information Systems

Volumn 44, Issue 2, 2015, Pages 243-269

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

^a UNIVERSITY OF SHIZUOKA   (Japan)

^b AOYAMA GAKUIN UNIVERSITY   (Japan)

^c RYUKOKU UNIVERSITY   (Japan)

^d OSAKA UNIVERSITY   (Japan)

^e UNIVERSITY OF TASMANIA   (Australia)

Author keywords

Burst detection; Change point detection; Information diffusion; Social networks

Indexed keywords

MAXIMUM LIKELIHOOD ESTIMATION; SOCIAL NETWORKING (ONLINE); TIME DELAY;

BURST DETECTION; CHANGE POINT DETECTION; EARTHQUAKE AND TSUNAMIS; EXPONENTIAL DISTRIBUTIONS; INFORMATION DIFFUSION; MAXIMUM LIKELIHOOD ESTIMATOR; STATE-OF-THE-ART METHODS; TIME DELAY PARAMETERS;

TREES (MATHEMATICS);

EID: 84925292269     PISSN: 09259902     EISSN: 15737675     Source Type: Journal    
DOI: 10.1007/s10844-013-0283-2     Document Type: Article

References (19)

1. Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19, 716–723.
2. Araujo, L., Cuesta, J.A., Merelo, J.J. (2006). Genetic algorithm for burst detection and activity tracking in event streams. In Proceedings of the 9th international conference on Parallel Problem Solving from Nature (PPSN’06) (pp. 302–311).
3. Bonacichi, P. (1987). Power and centrality: a family of measures. American Journal of Sociology, 92, 1170–1182.
4. Ebina, R., Nakamura, K., Oyanagi, S. (2011). A real-time burst detection method. In Proceedings of the 23rd IEEE International Conference on Tools with Artificial Intelligence (ICTAI) (pp. 1040–1046).
5. Goldenberg, J., Libai, B., Muller, E. (2001). Talk of the network: a complex systems look at the underlying process of word-of-mouth. Marketing Letters, 12, 211–223.
6. Katz, L. (1953). A new status index derived from sociometric analysis. Sociometry, 18, 39–43.
7. Kempe, D., Kleinberg, J., Tardos, E. (2003). 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) (pp. 137–146).
8. Kimura, M., Saito, K., Nakano, R., Motoda, H. (2010). Extracting influential nodes on a social network for information diffusion. Data Mining and Knowledge Discovery, 20, 70–97.
9. Kleinberg, J. (2002). Bursty and hierarchical structure in streams. In: Proceedings of the 8th ACM SIGKDD international conference on knowledge discovery and data mining (KDD-2002) (pp. 91–101).
10. Rissanen, J. (1989). Stochastic complexity in statistical inquiry. World Scientific.
11. Sadikov, E., Medina, M., Leskovec, J., Garcia-Molina, H. (2011). Correcting for missing data in information cascades. In Proceedings of the 4th ACM international conference on Web Search and Data Mining (WSDM 2011) (pp. 55–64).
12. Saito, K., Kimura, M., Ohara, K., Motoda, H. (2009). Learning continuous-time information diffusion model for social behavioral data analysis. In Proceedings of the 1st Asian Conference on Machine Learning (ACML2009), LNAI (Vol. 5828, pp. 322–337).
13. Saito, K., Kimura, M., Ohara, K., Motoda, H. (2010). Selecting information diffusion models over social networks for behavioral analysis. In Proceedings of the 2010 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2010), LNAI (Vol. 6323, pp. 180–195).
14. Sun, A., Zeng, D., Chen, H. (2010). Burst detection from multiple data streams: a network-based approach. IEEE Transactions on Systems, Man, & Cybernetics Society, Part C, 40, 258–267.
15. Wasserman, S., & Faust, K. (1994). Social network analysis. Cambridge, UK: Cambridge University Press.
16. Watts, D.J. (2002). A simple model of global cascades on random networks. Proceedings of National Academy of Science USA, 99, 5766–5771.
17. Watts, D.J., & Dodds, P.S. (2007). Influence, networks, and public opinion formation. Journal of Consumer Research, 34, 441–458.
18. Zhang, X. (2006). Fast algorithms for burst detection. PhD dissertation, New York University. http://pdf.aminer.org/000/301/507/better_burst_detection.pdf.
19. Zhu, Y., & Shasha, D. (2003). Efficient elastic burst detection in data streams. In Proceedings of the 9th ACM SIGKDD international conference on knowledge discovery and data mining (KDD-2003) (pp. 336–345).