Earticle

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

키워드

저자

• Kechen Zhuang [ School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China ]
• Fawang Han [ School of Information Technology, Nanjing Forest Police College, Nanjing, China ]
• Haibo Shen [ School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China ]
• Kun Zhang [ School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China ]
• Hong Zhang [ School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605