원문정보
보안공학연구지원센터(IJHIT)
International Journal of Hybrid Information Technology
Vol.9 No.6
2016.06
pp.327-336
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
Information diffusion in online social network especially in microblog system, can largely affect the public opinion and even the development of events, so the prediction of the future dynamics of diffusion could be very valuable at many aspects. In this research, a novel graph-based cascades construct algorithm is proposed, with which we build a prediction model for future information diffusion dynamics. By learning user influence features that related with the network topology and users’ interactions from a large scale real Weibo dataset, we successfully predicted the short term topic related tweets population dynamics.
목차
Abstract
1. Introduction
2. Related Works
3. Data Processing
4. Diffusion Cascades
5. Modeling
5.1. Features Analysis
5.2. Diffusion Instance Dynamic Prediction
5.3. Diffusion time prediction
6. Experiment
7. Conclusion
Acknowledgements
References
1. Introduction
2. Related Works
3. Data Processing
4. Diffusion Cascades
5. Modeling
5.1. Features Analysis
5.2. Diffusion Instance Dynamic Prediction
5.3. Diffusion time prediction
6. Experiment
7. Conclusion
Acknowledgements
References
저자정보
참고문헌
자료제공 : 네이버학술정보