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 $(1-{1}/{e}-\epsilon )$ -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 article, we present a novel and efficient parallel implementation of a RIS-based algorithm, namely IMM, on GPU. The proposed GPU-accelerated influence maximization algorithm, named gIM, can significantly reduce the running time on large-scale graphs with low values of $\epsilon$ . Furthermore, we show that gIM 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 ×. The source code of gIM is publicly available online.

中文翻译：

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gIM：基于RIS的GPU加速影响最大化算法

给定一个建模为加权图的社交网络 $ G $ ，影响最大化问题寻求 $ k $顶点最初受到影响，以在特定扩散模型下最大化受影响节点的预期数量。已经证明影响最大化问题是NP难的，并且针对该问题提出的大多数解决方案都是近似贪婪算法，相对于最优解决方案，该算法可以保证其结果具有可调的近似比率。最新的算法基于反向影响采样（RIS）技术，可以同时提供计算效率和非平凡的$（1- {1} / {e}-\ epsilon）$ -近似值保证 $ \ epsilon> 0 $ 。尽管基于RIS的算法与其他方法相比具有较低的计算成本，但仍需要较长的运行时间来解决具有较低s值的大规模图形中的问题。$ \ epsilon $ 。在本文中，我们提出了一种新颖高效的基于RIS的算法（即IMM）在GPU上的并行实现。所提出的GPU加速影响最大化算法gIM可以显着减少在低值的大型图上的运行时间。$ \ epsilon $ 。此外，我们证明了gIM算法仅需进行较小的修改就可以解决IM问题的其他变体。实验结果表明，所提出的解决方案将运行时间减少了多达220倍。gIM的源代码可在线公开获得。