How to cite this paper
Firdaniza, F., Ruchjana, B., Chaerani, D & Radianti, J. (2023). Non-homogeneous continuous time Markov chain model for information dissemination on Indonesian Twitter users.International Journal of Data and Network Science, 7(4), 1595-1602.
Refrences
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Begun, A., Icks, A., Waldeyer, R., Landwehr, S., Koch, M., & Giani, G. (2013). Identification of a multistate continuous-time nonhomogeneous markov chain model for patients with decreased renal function. Medical Decision Making, 33(2), 298–306. https://doi.org/10.1177/0272989X12466731
Bremaud, P. (2020) Probability Theory and Stochastic Processes, Switzerland, Springer.
Firdaniza, F., Ruchjana, B. N., Chaerani, D., & Radianti, J. (2022a). Information Diffusion Model in Twitter: A Systematic Literature Review. Information, 13(1). https://doi.org/10.3390/info13010013
Firdaniza, Ruchjana, B. N., Chaerani, D., & Radianti, J. (2022b). Information diffusion model with homogeneous continuous time Markov chain on Indonesian Twitter users. International Journal of Data and Network Science, 6(3), 659–668. https://doi.org/10.5267/j.ijdns.2022.4.006
Firdaus, S. N., Ding, C., & Sadeghian, A. (2018). Retweet: A popular information diffusion mechanism – A survey paper. Online Social Networks and Media, 6, 26–40. https://doi.org/10.1016/j.osnem.2018.04.001
Hsieh, H. J., Chen, T. H. H., & Chang, S. H. (2002). Assessing chronic disease progression using non-homogeneous exponential regression Markov models: An illustration using a selective breast cancer screening in Taiwan. Statistics in Medicine, 21(22), 3369–3382. https://doi.org/10.1002/sim.1277
Hubbard, R. A., Inoue, L. Y. T., & Fann, J. R. (2008). Modeling nonhomogeneous Markov processes via time transformation. Biometrics, 64(3), 843-850.
Kalbfleisch, J. D., & Lawless, J. F. (1985). The analysis of panel data under a Markov assumption. Journal of the American Statistical Association, 80(392), 863–871. https://doi.org/10.1080/01621459.1985.10478195
Kumar, Sanjay, Saini, M., Goel, M., & Aggarwal, N. (2020). Modeling Information Diffusion in Online Social Networks Using SEI Epidemic Model. Procedia Computer Science, 171(2019), 672–678. https://doi.org/10.1016/j.procs.2020.04.073
Kwon, J. (2017). Effects of source influence and peer referrals on information diffusion in Twitter. In Industrial Management and Data Systems (Vol. 117, Issue 5, pp. 896–909). https://doi.org/10.1108/IMDS-07-2016-0290
Li, J., Peng, W., Li, T., Sun, T., Li, Q., & Xu, J. (2014). Social network user influence sense-making and dynamics prediction. Expert Systems with Applications. https://www.sciencedirect.com/science/article/pii/S0957417414001146
Ocaña-Rilola, R. (2002). Two methods to estimate homogenous Markov processes. Journal of Modern Applied Statistical Methods, 1(1), 131–138. https://doi.org/10.22237/jmasm/1020255480
Ocaña-Riola, R. (2005). Non-homogeneous Markov processes for biomedical data analysis. Biometrical Journal, 47(3), 369–376. https://doi.org/10.1002/bimj.200310114
Shuai, X., Ding, Y., Busemeyer, J., Chen, S., Sun, Y., &Tang, J. (2012). Modeling indirect influence on twitter. International Journal on Semantic Web and Information Systems. 8(4), 20-36. https://www.igi-global.com/article/content/75772
Tapson, J., Jin, C., van Schaik, A. and Etienne-Cummings, R. (2009). A first-order nonhomogeneous markov model for the response of spiking neurons stimulated by small phase-continuous signals. Neural Computation, 21(6), pp. 1554–1588. doi:10.1162/neco.2009.06-07-548.
Titman, A. C. (2011). Flexible nonhomogeneous Markov models for panel observed data. Biometrics. https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1541-0420.2010.01550.x
Trajstman, A. C. (2002). A Markov Chain Model for Newcastle Disease and its Relevance to the Intracerebral Pathogenicity Index. Biometrical Journal, 44(1), 43. https://doi.org/10.1002/1521-4036(200201)44:13.3.co;2-v
Varshney, D., Kumar, S., & Gupta, V. (2017). Predicting information diffusion probabilities in social networks: A Bayesian networks based approach. Knowledge-Based Systems, 133, 66–76. https://doi.org/10.1016/j.knosys.2017.07.003
Zheng, Z., Yang, H., Fu, Y., Fu, D., Podobnik, B., & Stanley, H. E. (2018). Factors influencing message dissemination through social media. Physical Review E, 97(6). https://doi.org/10.1103/PhysRevE.97.062306
Zhu, T., Wang, B., Wu, B., & Zhu, C. (2014). Maximizing the spread of influence ranking in social networks. Information Sciences, 278, 535–544. https://doi.org/10.1016/j.ins.2014.03.070
Begun, A., Icks, A., Waldeyer, R., Landwehr, S., Koch, M., & Giani, G. (2013). Identification of a multistate continuous-time nonhomogeneous markov chain model for patients with decreased renal function. Medical Decision Making, 33(2), 298–306. https://doi.org/10.1177/0272989X12466731
Bremaud, P. (2020) Probability Theory and Stochastic Processes, Switzerland, Springer.
Firdaniza, F., Ruchjana, B. N., Chaerani, D., & Radianti, J. (2022a). Information Diffusion Model in Twitter: A Systematic Literature Review. Information, 13(1). https://doi.org/10.3390/info13010013
Firdaniza, Ruchjana, B. N., Chaerani, D., & Radianti, J. (2022b). Information diffusion model with homogeneous continuous time Markov chain on Indonesian Twitter users. International Journal of Data and Network Science, 6(3), 659–668. https://doi.org/10.5267/j.ijdns.2022.4.006
Firdaus, S. N., Ding, C., & Sadeghian, A. (2018). Retweet: A popular information diffusion mechanism – A survey paper. Online Social Networks and Media, 6, 26–40. https://doi.org/10.1016/j.osnem.2018.04.001
Hsieh, H. J., Chen, T. H. H., & Chang, S. H. (2002). Assessing chronic disease progression using non-homogeneous exponential regression Markov models: An illustration using a selective breast cancer screening in Taiwan. Statistics in Medicine, 21(22), 3369–3382. https://doi.org/10.1002/sim.1277
Hubbard, R. A., Inoue, L. Y. T., & Fann, J. R. (2008). Modeling nonhomogeneous Markov processes via time transformation. Biometrics, 64(3), 843-850.
Kalbfleisch, J. D., & Lawless, J. F. (1985). The analysis of panel data under a Markov assumption. Journal of the American Statistical Association, 80(392), 863–871. https://doi.org/10.1080/01621459.1985.10478195
Kumar, Sanjay, Saini, M., Goel, M., & Aggarwal, N. (2020). Modeling Information Diffusion in Online Social Networks Using SEI Epidemic Model. Procedia Computer Science, 171(2019), 672–678. https://doi.org/10.1016/j.procs.2020.04.073
Kwon, J. (2017). Effects of source influence and peer referrals on information diffusion in Twitter. In Industrial Management and Data Systems (Vol. 117, Issue 5, pp. 896–909). https://doi.org/10.1108/IMDS-07-2016-0290
Li, J., Peng, W., Li, T., Sun, T., Li, Q., & Xu, J. (2014). Social network user influence sense-making and dynamics prediction. Expert Systems with Applications. https://www.sciencedirect.com/science/article/pii/S0957417414001146
Ocaña-Rilola, R. (2002). Two methods to estimate homogenous Markov processes. Journal of Modern Applied Statistical Methods, 1(1), 131–138. https://doi.org/10.22237/jmasm/1020255480
Ocaña-Riola, R. (2005). Non-homogeneous Markov processes for biomedical data analysis. Biometrical Journal, 47(3), 369–376. https://doi.org/10.1002/bimj.200310114
Shuai, X., Ding, Y., Busemeyer, J., Chen, S., Sun, Y., &Tang, J. (2012). Modeling indirect influence on twitter. International Journal on Semantic Web and Information Systems. 8(4), 20-36. https://www.igi-global.com/article/content/75772
Tapson, J., Jin, C., van Schaik, A. and Etienne-Cummings, R. (2009). A first-order nonhomogeneous markov model for the response of spiking neurons stimulated by small phase-continuous signals. Neural Computation, 21(6), pp. 1554–1588. doi:10.1162/neco.2009.06-07-548.
Titman, A. C. (2011). Flexible nonhomogeneous Markov models for panel observed data. Biometrics. https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1541-0420.2010.01550.x
Trajstman, A. C. (2002). A Markov Chain Model for Newcastle Disease and its Relevance to the Intracerebral Pathogenicity Index. Biometrical Journal, 44(1), 43. https://doi.org/10.1002/1521-4036(200201)44:13.3.co;2-v
Varshney, D., Kumar, S., & Gupta, V. (2017). Predicting information diffusion probabilities in social networks: A Bayesian networks based approach. Knowledge-Based Systems, 133, 66–76. https://doi.org/10.1016/j.knosys.2017.07.003
Zheng, Z., Yang, H., Fu, Y., Fu, D., Podobnik, B., & Stanley, H. E. (2018). Factors influencing message dissemination through social media. Physical Review E, 97(6). https://doi.org/10.1103/PhysRevE.97.062306
Zhu, T., Wang, B., Wu, B., & Zhu, C. (2014). Maximizing the spread of influence ranking in social networks. Information Sciences, 278, 535–544. https://doi.org/10.1016/j.ins.2014.03.070