Utilizing BDI agents and a topological theory for mining online social networks

Hao Lan Zhang*, Jiming LIU, Yanchun Zhang

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

1 Citation (Scopus)


Online social networks (OSN) are facing challenges since they have been extensively applied to different domains including online social media, e-commerce, biological complex networks, financial analysis, and so on. One of the crucial challenges for OSN lies in information overload and network congestion. The demands for efficient knowledge discovery and data mining methods in OSN have been rising in recent year, particularly for online social applications, such as Flickr, YouTube, Facebook, and LinkedIn. In this paper, a Belief-Desire-Intention (BDI) agent-based method has been developed to enhance the capability of mining online social networks. Current data mining techniques encounter difficulties of dealing with knowledge interpretation based on complex data sources. The proposed agent-based mining method overcomes network analysis difficulties, while enhancing the knowledge discovery capability through its autonomy and collective intelligence.

Original languageEnglish
Pages (from-to)479-494
Number of pages16
JournalFundamenta Informaticae
Issue number1-4
Publication statusPublished - 2013

Scopus Subject Areas

  • Theoretical Computer Science
  • Algebra and Number Theory
  • Information Systems
  • Computational Theory and Mathematics

User-Defined Keywords

  • agent networks
  • AOC
  • BDI agents
  • Online social networks


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