Given a social network of users with selection cost, the BudgetedInfluence Maximization Problem asks forselecting a subset of the nodes (known as seed nodes) within an allocated budget for initial activation to maximize the influence in thenetwork . We model this problem as aco\mbox{-}operative game where the users of the network are the players and fora group of users, the expected influence by them under the MaximumInfluence Arborences’ diffusion model is its utility . We show this is `non-convex’ and `sub-additive’ We propose an iterative algorithm for finding seed nodes . The proposed methodologies have been implemented, and anextensive set of experiments have been conducted with three publicly availablesocial network datasets . From the experiments, we observe that the seed set selected by the proposed methodities lead to more number of influence nodes . In particular, if the community structure ofthe network is exploited then there is an increase upto $2 \%$ in number ofinfluenced nodes, we say . In addition, we also show that the proposedmethodology can even be more effective when the community structures of thenetworks is exploited . The proposal methodologies are more effective in using the community

Author(s) : Suman Banerjee

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Keywords : nodes - network - influence - problem - users -

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