A heuristic approach to discovering user correlations from organized social stream data

Xiaokang Zhou, Qun Jin*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

31 Citations (Scopus)


Recently, with the widespread popularity of SNS (Social Network Service), such as Twitter, Facebook, people are increasingly accustomed to sharing feeling, experience and knowledge with each other on Internet. The high accessibility of these web sites has allowed the information to be spread across the social media more quickly and widely, which leads to more and more populations being engaged into this so-called social stream environment. All these make the organization of user relationships become increasingly important and necessary. In this study, we try to discover the potential and dynamical user correlations using those organized social streams in accordance with users’ current interests and needs, in order to assist the collaborative information seeking process. We develop a heuristic approach to build a Dynamically Socialized User Networking (DSUN) model, and define a set of measures (such as interest degree, and popularity degree) and concepts (such as complementary tie, weak tie, and strong tie), to discover and represent users’ current profiling and dynamical correlations. The corresponding algorithms are developed respectively. Finally, the architecture of the functional modules is presented, and the experiment results are demonstrated and discussed based on an application of the proposed model.

Original languageEnglish
Pages (from-to)11487-11507
Number of pages21
JournalMultimedia Tools and Applications
Issue number9
Publication statusPublished - 2017 May 1


  • Information seeking
  • Social data
  • Social network analysis
  • Stream metaphor
  • User correlation discovery

ASJC Scopus subject areas

  • Software
  • Media Technology
  • Hardware and Architecture
  • Computer Networks and Communications


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