Learning social infectivity in sparse low-rank networks using multi-dimensional hawkes processes

Journal of Machine Learning Research

Volumn 31, Issue , 2013, Pages 641-649

  Zhou, Ke ^a   Zha, Hongyuan ^a   Song, Le ^a  

^a GEORGIA INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; CONVEX OPTIMIZATION; OPTIMIZATION; SOCIAL NETWORKING (ONLINE); VIRTUAL REALITY;

ALTERNATING DIRECTION METHOD OF MULTIPLIERS; ESTIMATION PROCEDURES; MULTI DIMENSIONAL; MUTUAL EXCITATION; OPTIMIZATION APPROACH; OPTIMIZATION PROBLEMS; PREDICTIVE MODELING; UNDERLYING NETWORKS;

ECONOMIC AND SOCIAL EFFECTS;

EID: 84954223626     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Conference Paper

References (19)

1. L. Adamopoulos. Some counting and interval properties of the mutually-exciting processes. Journal of Applied Probability, 12(1): 78, Mar. 1975.
2. C. Blundell, K. Heller, and J. Beck. Modelling reciprocating relationships with Hawkes processes. NIPS, 2012.
3. S. Boyd. Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations and Trends in Machine Learning, 3(1): 1-122, 2010.
4. J. Eckstein and D. P. Bertsekas. On the Douglas Rachford splitting method and the proximal point algorithm for maximal monotone operators. Mathematical Programming, 55(1-3): 293-318, Apr. 1992.
5. D. Gabay and B. Mercier. A Dual Algorithm for the Solution of Nonlinear Variational Problems via Finite Element Approximation. Computers & Mathematics with Applications, 2(1): 17-40, Jan. 1976.
6. A. Goyal, F. Bonchi, and L. V. Lakshmanan. Learning influence probabilities in social networks. In Proceedings of the third ACM interna- tional conference on Web search and data mining - WSDM '10, page 241, New York, New York, USA, 2010. ACM Press.
7. A. G. Hawkes. Spectra of some self-exciting and mutually exciting point processes. Biometrika, 58(1): 83-90, 1971.
8. D. R. Hunter and K. Lange. A tutorial on MM algorithms. The American Statistician, (58), 2004.
9. M. Kolar, L. Song, A. Ahmed, and E. Xing. Estimating time-varying networks. The Annals of Applied Statistics, 4(1): 94-123, 2010.
10. E. Lewisa and G. Mohlerb. A Nonparametric EM algorithm for multiscale Hawkes processes. Jour- nal of Nonparametric Statistics, (1), 2011.
11. D. Marsan and O. Lengliné. Extending earthquakes' reach through cascading. Science (New York, N.Y.), 319(5866): 1076-9, Feb. 2008.
12. S. Myers and J. Leskovec. On the convexity of latent social network inference. NIPS, 2010.
13. W. Pan, M. Cebrian, W. Dong, T. Kim, J. Fowler, and A. Pentland. Modeling dynamical influence in human interaction patterns. Arxiv preprint arXiv: 1009.0240, page 19, Sept. 2010.
14. E. Richard, P.-A. Savalle, and N. Vayatis. Estimation of simultaneously sparse and low rank matrices. ICML, June 2012.
15. M. G. Rodriguez, D. Balduzzi, and B. Schölkopf. Uncovering the temporal dynamics of diffusion networks. Proceedings of the 28th International Conference on Machine Learning, pages 561-568, May 2011.
16. A. Simma and M. Jordan. Modeling events with cascades of Poisson processes. UAI, 2012.
17. N. Srebro. Learning with matrix factorizations. Thesis, 2004.
18. A. Stomakhin, M. B. Short, and A. L. Bertozzi. Reconstruction of missing data in social networks based on temporal patterns of interactions. In- verse Problems, 27(11): 115013, Nov. 2011.
19. I. M. Toke. "Market making" behaviour in an order book model and its impact on the bid-ask spread. Arxiv, page 17, Mar. 2010.