A Study on Influence Maximization in Complex Networks

MANI SAKETH, Chennapragada VSS, PRANAY, Kakarla, SUSARLA, Akhila, RAVI RAM KARTHIK, Dukka, TANGIRALA, Jaya Lakshmi and NANDINI, YV (2023). A Study on Influence Maximization in Complex Networks. In: BHAJETA, Vikrant, CARROLL, Fiona, TAVARES, João Manuel R. S., SENGHAR, Sandeep Singh and PEER, Peter, (eds.) Intelligent Data Engineering and Analytics. Proceedings of the 11th International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA 2023). Smart Innovation, Systems and Technologies (371). Singapore, Springer Nature Singapore, 111-119.

Full text not available from this repository.
Official URL: https://link.springer.com/chapter/10.1007/978-981-...
Link to published version:: https://doi.org/10.1007/978-981-99-6706-3_10


Influence maximization deals with finding the most influential subset from a given complex network. It is a research problem that can be resourceful for various markets, for instance, the advertising market. This study reviews the dominant algorithms in the field of influence propagation and maximization from a decade.

Item Type: Book Section
Additional Information: Series ISSN - 2190-3026 FICTA 2023 11-12 April, Cardiff, UK.
Identification Number: https://doi.org/10.1007/978-981-99-6706-3_10
Page Range: 111-119
SWORD Depositor: Symplectic Elements
Depositing User: Symplectic Elements
Date Deposited: 06 Mar 2024 13:08
Last Modified: 07 Mar 2024 12:54
URI: https://shura.shu.ac.uk/id/eprint/33360

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