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Parallel Greedy Algorithm to Multiple Influence Maximization in Social Network

机译:并行贪婪算法在社交网络中多重影响最大化

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Influence Maximization (IM) problem is to select k influential users to maximize the influence spread, which plays an important role in many real-world applications such as product recommendation, epidemic control, and network monitoring. Nowadays multiple kinds of information can propagate in online social networks simultaneously, but current literature seldom discuss about this phenomenon. Accordingly, in this article, we propose Multiple Influence Maximization (MIM) problem where multiple information can propagate in a single network with different propagation probabilities. The goal of MIM problems is to maximize the overall accumulative influence spreads of different information with the limit of seed budget k. To solve MIM problems, we first propose a greedy framework to solve MIM problems which maintains an 13 -approximate ratio. We further propose parallel algorithms based on semaphores, an inter-thread communication mechanism, which significantly improves our algorithms efficiency. Then we conduct experiments for our framework using complex social network datasets with 12k, 154k, 317k, and 1.1m nodes, and the experimental results show that our greedy framework outperforms other heuristic algorithms greatly for large influence spread and parallelization of algorithms reduces running time observably with acceptable memory overhead.
机译:影响最大化(IM)问题是选择K有影响力的用户来最大化影响扩散,这在许多现实世界推荐,疫情控制和网络监控等许多现实应用中起着重要作用。如今,多种信息可以同时在线社交网络传播,但目前的文献很少讨论这种现象。因此,在本文中,我们提出了多次影响最大化(MIM)问题,其中多个信息可以以不同的传播概率在单个网络中传播。 MIM问题的目标是最大限度地提高不同信息的整体累计影响,具有种子预算k的极限。为了解决MIM问题,我们首先提出了一种贪婪的框架来解决MIM问题,其保持13个批时的比例。我们进一步提出了基于信号量的并行算法,是一种线程间通信机制,这显着提高了我们的算法效率。然后,我们使用具有12K,154K,317K和1.1M节点的复杂的社交网络数据集进行我们的框架进行实验,实验结果表明,我们的贪婪框架优于其他启发式算法大大,对于大量影响,算法的扩散和并行化可视地减少运行时间具有可接受的记忆开销。

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