Social networks represent nowadays in many contexts the main source of information transmission and the way opinions and actions are influenced. For instance, generic advertisements are way less powerful than suggestions from our contacts. However, this process hugely depends on the influence of people who disseminate these suggestions. Therefore modern marketing often involves paying some targeted users, or influencers, for advertising products or ideas. Finding the set of nodes in a social network that lead to the highest information spread -- the so-called Influence Maximization (IM) problem -- is therefore a pressing question and as such it has recently attracted a great research interest. In particular, several approaches based on Evolutionary Algorithms (EAs) have been proposed, although they are known to scale poorly with the graph size. In this paper, we tackle this limitation in two ways. Firstly, we use approximate fitness functions to speed up the EA. Secondly, we include into the EA various graph-aware mechanisms, such as smart initialization, custom mutations and node filtering, to facilitate the EA convergence. Our experiments show that the proposed modifications allow to obtain a relevant runtime gain and also improve, in some cases, the spread results.

中文翻译：

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影响力最大化的图感知进化算法

如今，社交网络在许多情况下都代表着信息传递的主要来源以及意见和行动的影响方式。例如，一般性广告的功能远不及我们联系人的建议强大。但是，此过程在很大程度上取决于传播这些建议的人员的影响。因此，现代营销通常涉及向一些目标用户或有影响力的人支付广告产品或想法的费用。因此，在社交网络中寻找导致最大信息传播的节点集-所谓的影响力最大化（IM）问题-是一个紧迫的问题，因此，它最近引起了极大的研究兴趣。尤其是，尽管基于图形尺寸的缩放比例很差，但是已经提出了几种基于进化算法（EA）的方法。在本文中，我们通过两种方式解决此限制。首先，我们使用近似适应度函数来加速EA。其次，我们将各种图形感知机制（例如智能初始化，自定义突变和节点过滤）包含在EA中，以促进EA收敛。我们的实验表明，所提出的修改允许获得相关的运行时增益，并且在某些情况下还可以改善传播结果。