DQSSA: A Quantum-Inspired Solution for Maximizing Influence in Online Social Networks (Student Abstract)
This work addresses influence maximization for social network analysis, but it appears incremental as it builds on existing meta-heuristic and quantum-inspired approaches.
The study tackled the problem of influence maximization in social networks by proposing DQSSA, a quantum-inspired algorithm, which outperformed established methods on four real-world datasets.
Influence Maximization is the task of selecting optimal nodes maximising the influence spread in social networks. This study proposes a Discretized Quantum-based Salp Swarm Algorithm (DQSSA) for optimizing influence diffusion in social networks. By discretizing meta-heuristic algorithms and infusing them with quantum-inspired enhancements, we address issues like premature convergence and low efficacy. The proposed method, guided by quantum principles, offers a promising solution for Influence Maximisation. Experiments on four real-world datasets reveal DQSSA's superior performance as compared to established cutting-edge algorithms.