Material Detail

Scalable Influence Maximization for Prevalent Viral Marketing in Large-Scale Social Networks

Scalable Influence Maximization for Prevalent Viral Marketing in Large-Scale Social Networks

This video was recorded at 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), Washington 2010. Influence maximization, defined by Kempe, Kleinberg, and Tardos (2003), is the problem of finding a small set of seed nodes in a social network that maximizes the spread of influence under certain influence cascade models. The scalability of influence maximization is a key factor for enabling prevalent viral marketing in large-scale online social networks. Prior solutions, such as the greedy algorithm of Kempe et al. (2003) and its improvements are slow and not scalable, while other heuristic algorithms do not provide consistently good performance on influence spreads. In this paper, we design a new heuristic algorithm that is easily scalable to millions of... Show More

Quality

  • User Rating
  • Comments
  • Learning Exercises
  • Bookmark Collections
  • Course ePortfolios
  • Accessibility Info

More about this material

Browse...

Disciplines with similar materials as Scalable Influence Maximization for Prevalent Viral Marketing in Large-Scale Social Networks

Comments

Log in to participate in the discussions or sign up if you are not already a MERLOT member.
hidden