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 $(1-\frac{1}{e}-\epsilon)$-approximation ratio guarantee for any $\epsilon >0$. 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 $\epsilon$. 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 $\epsilon$. 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 $220 \times$.

中文翻译：

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基于 GPU 加速的 RIS 影响最大化算法

给定一个建模为加权图 $G$ 的社交网络，影响最大化问题寻求 $k$ 顶点最初受到影响，以最大化特定扩散模型下受影响节点的预期数量。影响最大化问题已被证明是 NP-hard 问题，该问题的大多数解决方案都是近似贪婪算法，它可以保证其结果相对于最优解的近似率可调。最先进的算法基于反向影响采样 (RIS) 技术，它可以提供计算效率和非平凡的 $(1-\frac{1}{e}-\epsilon)$-近似比保证任何 $\epsilon >0$。尽管与其他方法相比，基于 RIS 的算法计算成本较低，在 $\epsilon$ 值较低的大规模图中，仍然需要很长的运行时间来解决问题。在本文中，我们在 GPGPU 上提出了一种新颖且高效的基于 RIS 算法的并行实现，即 IMM。所提出的解决方案可以显着减少 $\epsilon$ 值较低的大规模图的运行时间。此外，我们表明我们提出的并行算法可以解决 IM 问题的其他变体，只需应用微小的修改。实验结果表明，所提出的解决方案将运行时间减少了高达 $220 \times$。所提出的解决方案可以显着减少 $\epsilon$ 值较低的大规模图的运行时间。此外，我们表明我们提出的并行算法可以解决 IM 问题的其他变体，只需应用微小的修改。实验结果表明，所提出的解决方案将运行时间减少了高达 $220 \times$。所提出的解决方案可以显着减少 $\epsilon$ 值较低的大规模图的运行时间。此外，我们表明我们提出的并行算法可以解决 IM 问题的其他变体，只需应用微小的修改。实验结果表明，所提出的解决方案将运行时间减少了高达 $220 \times$。