A Multiobjective Evolutionary Approach for Influence Maximization in Multilayer Networks

Qipeng Lu, Zhan Bu, Yuyao Wang
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引用次数: 2

Abstract

Influence Maximization (IM) is one key algorithmic problems in information diffusion research; it aims to select a set of users from a social network and, by following a specific model, maximize the number of users influenced (the influence spread). Yet despite its immense potential, relatively little research is dedicated to IM for multilayer networks. Conversely, most existing IM studies that rely on a greedy algorithm strategy only obtain a single solution that provides limited insights on the target networks' core organization. With that in mind, we focus on studying the Influence Maximization Problem (IMP) in multilayer networks. Specifically, we define novel concepts, such as the pairwise reciprocal length and pairwise influence, with respect to the information-diffusion process in multilayer networks. Then we formulate the IM in multilayer networks as a multiobjective optimization problem and employ the classic Nondominated Sorting Genetic Algorithm II (NSGA-II) to find a set of Pareto-optimal solutions that provide a wide range of options for decision makers. To maintain population diversity and accelerate the algorithm's convergence, we combine a heuristic population initialization strategy and an efficient two-point crossover operation. Extensive experiments show that our approach has competitive performance when compared to off-the-shelf IM algorithms with regard to influence spread and running time.
多层网络中影响最大化的多目标进化方法
影响最大化(IM)是一个关键算法信息扩散研究中存在的问题;它旨在从社交网络中选择一组用户,并遵循特定的模型,使受影响的用户数量(影响传播)最大化。然而,尽管具有巨大的潜力,针对多层网络的即时通讯的研究相对较少。相反,大多数现有的IM研究依赖于贪婪算法策略,只能获得单一的解决方案,对目标网络的核心组织提供有限的见解。考虑到这一点,我们重点研究了多层网络中的影响最大化问题。具体来说,我们定义了关于多层网络中信息扩散过程的新概念,如成对互易长度和成对影响。然后我们制定IM在多层网络作为多目标优化问题,采用经典Nondominated第二排序遗传算法(NSGA-II)找到一组帕累托最优解决方案,为决策者提供广泛的选择。为了保持种群多样性和加速算法收敛,我们将启发式种群初始化策略与高效两点交叉操作相结合。大量实验表明,与现成的IM算法相比,我们的方法在影响传播和运行时间方面具有竞争力。
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