ML Anthology

DQSSA: A Quantum-Inspired Solution for Maximizing Influence in Online Social Networks (Student Abstract)

Aryaman Rao, Parth Singh, Dinesh Kumar Vishwakarma, Mukesh Prasad
AAAI 2024 pp. 23628-23630
doi:10.1609/AAAI.V38I21.30501 /aaai/2024/rao2024aaai-dqssa/

Abstract

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.

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Cite

Text

Rao et al. "DQSSA: A Quantum-Inspired Solution for Maximizing Influence in Online Social Networks (Student Abstract)." AAAI Conference on Artificial Intelligence, 2024. doi:10.1609/AAAI.V38I21.30501

Markdown

[Rao et al. "DQSSA: A Quantum-Inspired Solution for Maximizing Influence in Online Social Networks (Student Abstract)." AAAI Conference on Artificial Intelligence, 2024.](https://mlanthology.org/aaai/2024/rao2024aaai-dqssa/) doi:10.1609/AAAI.V38I21.30501

BibTeX

@inproceedings{rao2024aaai-dqssa,
  title     = {{DQSSA: A Quantum-Inspired Solution for Maximizing Influence in Online Social Networks (Student Abstract)}},
  author    = {Rao, Aryaman and Singh, Parth and Vishwakarma, Dinesh Kumar and Prasad, Mukesh},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2024},
  pages     = {23628-23630},
  doi       = {10.1609/AAAI.V38I21.30501},
  url       = {https://mlanthology.org/aaai/2024/rao2024aaai-dqssa/}
}