Exploiting small world property for network clustering

Tieyun Qian, Qing Li, Jaideep Srivastava, Zhiyong Peng, Yang Yang, Shuo Wang

Research output: Contribution to journalArticle

3 Citations (Scopus)


Graph partitioning is a traditional problem with many applications and a number of high-quality algorithms have been developed. Recently, demand for social network analysis arouses the new research interest on graph partitioning/clustering. Social networks differ from conventional graphs in that they exhibit some key properties like power-law and small-world property. Currently, these features are largely neglected in popular partitioning algorithms. In this paper, we present a novel framework which leverages the small-world property for finding clusters in social networks. The framework consists of several key features. Firstly, we define a total order, which combines the edge weight, the small-world weight, and the hub value, to better reflect the connection strength between two vertices. Secondly, we design a strategy using this ordered list, to greedily, yet effectively, refine existing partitioning algorithms for common objective functions. Thirdly, the proposed method is independent of the original approach, such that it could be integrated with any types of existing graph clustering algorithms. We conduct an extensive performance study on both real-life and synthetic datasets. The empirical results clearly demonstrate that our framework significantly improves the output of the state-of-the-art methods. Furthermore, we show that the proposed method returns clusters with both internal and external higher qualities.

Original languageEnglish
Pages (from-to)405-425
Number of pages21
JournalWorld Wide Web
Issue number3
Publication statusPublished - 1 May 2014
Externally publishedYes



  • graph partitioning
  • network clustering
  • small world property

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Hardware and Architecture
  • Software

Cite this