Earticle

초록

영어

A large number of emerging information networks brings new challenges to the community detection. The meaningful community should be topic-oriented. However, the topology-based methods only reflect the strength of connection, and ignore the consistency of the topics; the content-based methods focus on the contents and completely ignore the links. This paper explores a topic oriented community detection method simLPA based on label propagation for information work. The method utilizes Latent Dirichlet Allocation topic model to represent the node content, and calculate the content similarity by the normalized Kullback–Leibler divergence. simLPA extended by LabelRank fuses the links and the contents naturally to detect the topic community. Extensive experiments on nine real-world datasets with varying sizes and characteristics validate the proposed method outperforms other baseline algorithms in quality. Additionally simLPA integrated into the content is equivalent to LabelRank in efficiency, which is easy to handle large-scale information networks.

목차

Abstract
 1. Introduction
 2. Related Works
 3. Methodology
  3.1. The Content Representation
  3.2 simLPA Algorithm
  3.3 Performance Metrics
 4. Experiments
  4.1 Datasets
  4.2 Effect of Inflation Factor
  4.3 The Impact of the Different Label Propagation Strategy
  4.4 Running Time
  4.5 Community Detection Results
 5. Conclusions
 References

키워드

저자

• SHEN Gui-Lan [ Information School, Renmin University, Beijng, China, Business Collage of Beijing Union University, Beijing, China ]
• YANG Xiao-Ping [ Information School, Renmin University, Beijng, 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