Given a fixed total budget and a predefined cost model, the budgeted influence maximization problem aims to find a subset of nodes to maximize the influence spread in social networks while its cost should be no more than the fixed total budget. In this paper, we propose a local-global influence indicator based constrained evolutionary algorithm, named IICEA, to solve the budgeted influence maximization problem effectively and efficiently. In IICEA, a novel influence indicator is firstly designed by considering two components: local neighbor information and global community information, which can be used to better measure the influence of nodes in social networks. Based on the proposed local-global influence indicator, we propose a constrained evolutionary framework by designing several novel strategies such as mutation strategy, crossover strategy and repairment strategy to promote the evolution of population. Experimental results on 10 real-world social networks demonstrate the effectiveness of the proposed local-global influence indicator and also verify the effectiveness and efficiency of the proposed algorithm IICEA in comparison with several variant versions of IICEA and several representative baseline algorithms.

中文翻译：

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基于局部-全局影响指标的社交网络预算影响最大化约束进化算法

给定固定的总预算和预定义的成本模型，预算影响最大化问题旨在找到一个节点子集，以最大化社交网络中的影响力传播，而其成本不应超过固定的总预算。在本文中，我们提出了一种基于局部-全局影响指标的约束进化算法，名为 IICEA，以有效地解决预算影响最大化问题。在IICEA中，首先通过考虑本地邻居信息和全球社区信息两个组成部分设计了一种新颖的影响力指标，可以用来更好地衡量社交网络中节点的影响力。基于提出的局部-全局影响指标，我们通过设计几种新的策略，如变异策略，提出了一个约束进化框架，交叉策略和修复策略促进种群进化。在 10 个真实世界社交网络上的实验结果证明了所提出的本地-全球影响力指标的有效性，并与 IICEA 的几个变体版本和几个代表性基线算法进行了比较，验证了所提出的算法 IICEA 的有效性和效率。