Influence maximization in social networks using discrete BAT-modified (DBATM) optimization algorithm: a computationally intelligent viral marketing approach
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Science Applications,Human-Computer Interaction,Media Technology,Communication,Information Systems
Link
https://link.springer.com/content/pdf/10.1007/s13278-023-01151-3.pdf
Reference53 articles.
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2. Aggarwal K, Arora A (2022) Influence maximization for MOOC learners using BAT optimization algorithm. Int J Fuzzy Syst Appl 11(2):1–19. https://doi.org/10.4018/ijfsa.296588
3. Aghaee Z, Ghasemi MM, Beni HA, Bouyer A, Fatemi A (2021) A survey on meta-heuristic algorithms for the influence maximization problem in the social networks. Computing 103(11):2437–2477. https://doi.org/10.1007/s00607-021-00945-7
4. Ahn CW (2006) Practical genetic algorithms. In: Studies in computational intelligence 18. https://doi.org/10.1007/11543138_2
5. Akbar Z, Liu J, Latif Z (2021) Mining social applications network from business perspective using modularity maximization for community detection. Soc Netw Anal Min 11(1):1–19. https://doi.org/10.1007/s13278-021-00798-0
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