Social network analysis and mining for business applications

Francesco Bonchi, Carlos Castillo, Aristides Gionis, Alejandro Jaimes

Research output: Contribution to journalArticle

168 Citations (Scopus)


Social network analysis has gained significant attention in recent years, largely due to the success of online social networking and media-sharing sites, and the consequent availability of a wealth of social network data. In spite of the growing interest, however, there is little understanding of the potential business applications of mining social networks. While there is a large body of research on different problems and methods for social network mining, there is a gap between the techniques developed by the research community and their deployment in real-world applications. Therefore the potential business impact of these techniques is still largely unexplored. In this article we use a business process classiication framework to put the research topics in a business context and provide an overview of what we consider key problems and techniques in social network analysis and mining from the perspective of business applications. In particular, we discuss data acquisition and preparation, trust, expertise, community structure, network dynamics, and information propagation. In each case we present a brief overview of the problem, describe state-of-the art approaches, discuss business application examples, and map each of the topics to a business process classification framework. In addition, we provide insights on prospective business applications, challenges, and future research directions. The main contribution of this article is to provide a state-of-the-art overview of current techniques while providing a critical perspective on business applications of social network analysis and mining.

Original languageEnglish
Article number22
JournalACM Transactions on Intelligent Systems and Technology
Issue number3
Publication statusPublished - 1 Apr 2011
Externally publishedYes



  • Community structure
  • Expert finding
  • Influence propagation
  • Networks dynamics and evolution
  • Social networks
  • Viral marketing

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Artificial Intelligence

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