Balancing Spreads of Influence in a Social Network


  • Ruben Becker Gran Sasso Science Institute
  • Federico Corò Gran Sasso Science Institute
  • Gianlorenzo D'Angelo Gran Sasso Science Institute
  • Hugo Gilbert Gran Sasso Science Institute



The personalization of our news consumption on social media has a tendency to reinforce our pre-existing beliefs instead of balancing our opinions. To tackle this issue, Garimella et al. (NIPS'17) modeled the spread of these viewpoints, also called campaigns, using the independent cascade model introduced by Kempe, Kleinberg and Tardos (KDD'03) and studied an optimization problem that aims to balance information exposure when two opposing campaigns propagate in a network. This paper investigates a natural generalization of this optimization problem in which μ different campaigns propagate in the network and we aim to maximize the expected number of nodes that are reached by at least ν or none of the campaigns, where μν ≥ 2. Following Garimella et al., despite this general setting, we also investigate a simplified one, in which campaigns propagate in a correlated manner. While for the simplified setting, we show that the problem can be approximated within a constant factor for any constant μ and ν, for the general setting, we give reductions leading to several approximation hardness results when ν ≥ 3. For instance, assuming the gap exponential time hypothesis to hold, we obtain that the problem cannot be approximated within a factor of ng(n) for any g(n) = o(1) where n is the number of nodes in the network. We complement our hardness results with an Ω(n−1/2)-approximation algorithm for the general setting when ν = 3 and μ is arbitrary.




How to Cite

Becker, R., Corò, F., D’Angelo, G., & Gilbert, H. (2020). Balancing Spreads of Influence in a Social Network. Proceedings of the AAAI Conference on Artificial Intelligence, 34(01), 3-10.



AAAI Technical Track: AI and the Web