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Due to much closer to real application scenarios,the budgeted influencemaximization (BIM) problem has attracted great attention among researchers. Asa variant of the influence maximization (IM) problem, the BIM problem aims atmining several nodes with different costs as seeds with limited budget tomaximize the influence as possible. By first activating these seed nodes andspreading influence under the given propagation model, the maximized spread ofinfluence can be reached in the network.  Several approaches have been proposed for BIM. Most of them are modifiedversions of the greedy algorithm, which work well on the IM but seemsinefficient for the BIM because huge time consuming is inevitable. Recently,some intelligence algorithms are proposed in order to reduce the running time,but analysis shows that they cannot fully utilize the relationships betweennodes in networks, which will result in influence loss.  Inspired by this, we propose an efficient method based on boosted simulatedannealing (SA) algorithm in this paper. Three heuristic strategies are proposedto improve the performance and speed up the proposed algorithm. Experimentalresults on both real world and synthetic networks demonstrate that the proposedboosted SA performs much better than existed algorithms on performance withalmost equal or less running time.

Budgeted Influence Maximization via Boost Simulated Annealing in Social Networks