GPU Accelerated RIS-based Influence Maximization Algorithm
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Given a social network modeled as a weighted graph G, the influence maximization problem seeks k vertices to become initially influenced, to maximize the expected number of influenced nodes under a particular diffusion model. The influence maximization problem has been proven to be NP-hard, and most proposed solutions to the problem are approximate greedy algorithms, which can guarantee a tunable approximation ratio for their results with respect to the optimal solution. The state-of-the-art algorithms are based on Reverse Influence Sampling (RIS) technique, which can offer both computational efficiency and non-trivial (1-1/e-ϵ)-approximation ratio guarantee for any ϵ >0. RIS-based algorithms, despite their lower computational cost compared to other methods, still require long running times to solve the problem in large-scale graphs with low values of ϵ. In this paper, we present a novel and efficient parallel implementation of a RIS-based algorithm, namely IMM, on GPGPU. The proposed solution can significantly reduce the running time on large-scale graphs with low values of ϵ. Furthermore, we show that our proposed parallel algorithm can solve other variations of the IM problem, only by applying minor modifications. Experimental results show that the proposed solution reduces the runtime by a factor up to 220 ×.
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