Abstract
Influence maximization is to identify a subset of nodes at which if the information is released, the information spread can be maximized. Faisan and Bhavani [7] proposed incorporating greedy selection in the initialization step of RankedReplace algorithm of Charu Aggarwal et al. which would speed up the algorithm. We propose to improve this algorithm further by considering novel heuristic called influential degree for selection of the initial set. The experiments are carried out on small as well as large data sets like DBLP and the results show that RRID and its variations perform quite well on all the data sets quite efficiently reducing the time taken and retaining, and in a few cases, obtaining much better influence spread than the original RankedReplace algorithm.
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Mallesham, J., Bhavani, S.D. (2016). Influential Degree Heuristic for RankedReplace Algorithm in Social Networks. In: Bjørner, N., Prasad, S., Parida, L. (eds) Distributed Computing and Internet Technology. ICDCIT 2016. Lecture Notes in Computer Science(), vol 9581. Springer, Cham. https://doi.org/10.1007/978-3-319-28034-9_7
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DOI: https://doi.org/10.1007/978-3-319-28034-9_7
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