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Dynamic influence maximization problem (DIMP) aims to maintain a group ofinfluential users within an evolving social network, so that the influencescope can be maximized at any given moment. A primary category of DIMPalgorithms focuses on the renewal of reverse reachable (RR) sets, which isdesigned for static social network scenarios, to accelerate the estimation ofinfluence spread. And the generation time of RR sets plays a crucial role inalgorithm efficiency. However, their update approaches require sequentialupdates for each edge change, leading to considerable computational cost. Inthis paper, we propose a strategy for batch updating the changes in networkedge weights to efficiently maintain RR sets. By calculating the probabilitythat previous RR sets can be regenerated at the current moment, we retain thosewith a high probability. This method can effectively avoid the computationalcost associated with updating and sampling these RR sets. Besides, we proposean resampling strategy that generates high-probability RR sets to make thefinal distribution of RR sets approximate to the sampling probabilitydistribution under the current social network. The experimental resultsindicate that our strategy is both scalable and efficient. On the one hand,compared to the previous update strategies, the running time of our strategy isinsensitive to the number of changes in network weight; on the other hand, forvarious RR set-based algorithms, our strategy can reduce the running time whilemaintaining the solution quality that is essentially consistent with the staticalgorithms.

A Sample Reuse Strategy for Dynamic Influence Maximization Problem