  Influence Maximization (IM) is the task of determining k optimal influential
nodes in a social network to maximize the influence spread using a propagation
model. IM is a prominent problem for viral marketing, and helps significantly
in social media advertising. However, developing effective algorithms with
minimal time complexity for real-world social networks still remains a
challenge. While traditional heuristic approaches have been applied for IM,
they often result in minimal performance gains over the computationally
expensive Greedy-based and Reverse Influence Sampling-based approaches. In this
paper, we propose the discretization of the nature-inspired Harris Hawks
Optimisation meta-heuristic algorithm using community structures for optimal
selection of seed nodes for influence spread. In addition to Harris Hawks
intelligence, we employ a neighbour scout strategy algorithm to avoid blindness
and enhance the searching ability of the hawks. Further, we use a candidate
nodes-based random population initialization approach, and these candidate
nodes aid in accelerating the convergence process for the entire populace. We
evaluate the efficacy of our proposed DHHO approach on six social networks
using the Independent Cascade model for information diffusion. We observe that
DHHO is comparable or better than competing meta-heuristic approaches for
Influence Maximization across five metrics, and performs noticeably better than
competing heuristic approaches.
