Heuristic Algorithms for Influence Maximization in Partially Observable Social Networks

Abstract We consider the problem of selecting the most influential members within a social network, in order to disseminate a message as widely as possible. This problem, also referred to as seed selection for influence maximization, has been under intensive investigation since the emergence of social networks. Nonetheless, a large body of existing research is based on the assumption that the network is completely known, whereas little work considers partially observable networks. Yet, due to many issues including the extremely large size of current networks and privacy considerations, assuming full knowledge of the network is rather unrealistic. Despite this, an influencer often wishes to distribute its message far beyond the boundaries of the known network. In this paper, we propose a set of novel heuristic algorithms that specifically target nodes at this boundary, in order to maximize influence across the whole network. We show that these algorithms outperform the state of the art by up to 38% in networks with partial observability.
  • Sebastian Stein (Southampton)
  • Soheil Eshghi (Yale)
  • Setareh Maghsudi (Yale)
  • Leandros Tassiulas (Yale)
  • Rachel Bellamy (IBM US)
  • Nick Jennings (Imperial)
Date Aug-2017
Venue 3rd International Workshop on Social Influence Analysis