Earticle

초록

영어

Aiming at the division roughness of topic classification existing in the most online social networks community, the improved particle swarm optimization algorithm is applied to refine community topics and concepts of community seeds and community topic are also introduced. In this paper, first of all, the explicit links existing in the community are mined, and the basic community structure is constructed, then the community content is deeply analyzed, according to implicit feature between nodes under online community, community topic categories are elaborately refined until structure is stable. Experiments show that this proposed algorithm can accelerate the convergence of the node and greatly improves the topic mining accuracy of online social network compared with the state-of-art CR2NDAS model and PLSA model.

목차

Abstract
 1. Introduction
 2. Related Work
 3. Introduction of PSO Algorithm
 4. The Proposed Community Topic Refinement Strategy Based on PSO Algorithm
 5. Experimental Results and Analysis
  5.1 Datasets and Setting
  5.2 Experiment One-Average-Radius Changing Trend
  5.3 Experiment Two-Comparative Experiments
 6. Conclusion and Future Work
 References

키워드

저자

• Lin Cui [ Intelligent Information Processing Laboratory, Suzhou University, Suzhou 234000, Anhui, China, College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, Jiangsu, China ]
• Caiyin Wang [ Intelligent Information Processing Laboratory, Suzhou University, Suzhou 234000, Anhui, China ]
• Xiaoyin Wu [ Intelligent Information Processing Laboratory, Suzhou University, Suzhou 234000, Anhui, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Future Generation Communication and Networking

• 간기

  격월간

• pISSN

  2233-7857

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605