Earticle

초록

영어

Community detection is an important issue in social network analysis, which aims at finding potential community structures such that the internal nodes of a community have higher closeness than external nodes. Taking into account node attribute information, this paper presents an improved community detection algorithm based on random walk. Based on the basic understanding that people getting together often relies on their common interests, node similarities are initially calculated with node attributes and iteratively updated based on the random walk model. Meanwhile, node importance is computed to represent how much it can influence other nodes, based on which some important nodes are selected as seeds for community clustering. As for overlapping community detection, some construction is made on a given social network. Experimental results on several real datasets show our approach has better effects than previous methods on both overlapping and non- overlapping communities.

목차

Abstract
 1. Introduction
 2. Related Work
  2.1. Non-overlapping Community Detection Algorithms
  2.2. Overlapping Community Detection Algorithms
 3. Community Detection based on Random Walk and Node Attributes
  3.1. Attributes Processing and Node Rating
  3.2. Community Detection Algorithm
  3.2. Community Metric
 4. Experiments and Analysis
  4.1. Data Sets
  4.2. Discussion on Parameter Setting
  4.3. Comparison with Related Methods
 5. Conclusion and Future Work
 Acknowledgements
 References

키워드

저자

• Daxiang Ji [ School of Computer Science and Technology Shandong University, Jinan 250100 ]
• Yuqing Sun [ School of Computer Science and Technology Shandong University, Jinan 250100 ]
• Demin Li [ School of Computer Science and Technology Shandong University, Jinan 250100 ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605