Earticle

초록

영어

Heterogeneous networks, composed of multiple types of objects and relationships, are ubiquitous in real life. Although many methods have been proposed for community detection in homogeneous networks which contain only one type of objects and one type of relationships between these objects, effective direct clustering objects of all types in heterogeneous networks without heterogeneous-to-homogeneous transformation remains a challenge. To achieve this goal, we propose a three-phase method for clustering star-structured heterogeneous data based on diffusion path. By adopting the principle that central objects are more important than attribute objects, we firstly assess the proximity of central objects in terms of their connected objects of all types, then based on which we cluster central objects, and thirdly we detect attribute objects groups according to their associated central objects. Finally, experiments on a real-world data set show the effectiveness and efficiency of the proposed methods.

목차

Abstract
 1. Introduction
 2. Our Method
  2.1. Concepts
  2.2. The General Framework
  2.3. Method for Similarity Measurement Between Central Objects
  2.4. Method for Central Object Clustering
  2.5. Method for Attribute Object Clustering
 3. Experimental Results
 4. Conclusions
 Acknowledgements
 References

키워드

저자

• Huang Yue [ Library of Beijing Language and Culture University, Beijing, P. R. China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605