Heuristic Algorithms for Influence Maximization with Partial Information

Abstract Models to study the propagation of opinions and influence in social networks have been extensively studied and, in particular, the computer science literature has focused on developing algorithms to maximise the spread of influence. However, little work considers the common real-world scenario in which only portions of the full network are visible or only a subset of nodes can be chosen to spread influence from. In particular, in this paper we explore influence maximisation under a type of uncertainty which has not been investigated so far. In our setting, a part (or some parts) of a network is known (e.g., individuals that belong to the decision maker's organisation), while the rest is completely unobservable. We propose a set of heuristic algorithms designed to maximise the spread of influence in such a setting, by preferentially targeting boundary nodes. We consider the case of organisation-partitioned networks, i.e., networks in which a subset of nodes (a community) and all links among them are fully visible, but the rest are unknown. We show that, in such a setting, the proposed algorithms outperform the state of the art by up to 38%.
  • Soheil Eshghi (Yale)
  • Setareh Maghsudi (Yale)
  • Valerio Restocchi (Southampton)
  • Leandros Tassiulas (Yale)
  • Rachel Bellamy (IBM US)
  • Nick Jennings (Imperial)
  • Sebastian Stein (Southampton)
Date Sep-2017
Venue 1st Annual Fall Meeting of the DAIS ITA, 2017