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SEIM: Search economics for influence maximization in online social networks

机译：SEIM：搜索经济学以寻求在线社交网络中的影响力最大化

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摘要

The influence of online social networks (OSN), which can be regarded as part of our life, is evident today. As expected, a great deal of useful information about the humans is hidden in the data, such as interpersonal relationship and personal preference. The influence maximization problem (IMP) is one of the well-known problems in this research domain that has attracted the attention of researchers from different disciplines in recent years. One of the reasons is that it can speed up the propagation of information in OSN if we can find out users that have maximum influence on other users. However, traditional rule-based and heuristic algorithms may not be able to find useful information out of these data because the data are generally large and complex. Although many recent studies attempted to use metaheuristic algorithms to solve the IMP, there is still plenty of room for improvement. The proposed algorithm, called search economics for influence maximization (SEIM), is motivated by the concept of return on investment to design its search strategies. As far as the proposed algorithm is concerned, the search strategy of SEIM is like making a good plan to determine how to invest the high potential investment subjects (i.e., regions) in the market (i.e., search space). The experimental results show that the proposed algorithm is significantly better than the other state-of-the-art influence maximization problem algorithms compared in this paper in terms of the quality of the end result and the number of objective function evaluations. (C) 2018 Elsevier B.V. All rights reserved.
机译：今天，可以将在线社交网络（OSN）视为我们生活的一部分而产生的影响显而易见。不出所料，数据中隐藏了许多有关人类的有用信息，例如人际关系和个人喜好。影响最大化问题（IMP）是该研究领域中的著名问题之一，近年来引起了来自不同学科的研究人员的关注。原因之一是，如果我们可以找出对其他用户影响最大的用户，则可以加快OSN中信息的传播。但是，传统的基于规则和启发式算法可能无法从这些数据中找到有用的信息，因为数据通常很大且很复杂。尽管最近的许多研究试图使用元启发式算法来解决IMP，但仍有很大的改进空间。该算法被称为“影响最大化的搜索经济学”（SEIM），其灵感来自于投资回报率概念，以设计其搜索策略。就所提出的算法而言，SEIM的搜索策略就像制定一个好的计划，以确定如何在市场（即搜索空间）中投资高潜力投资主体（即区域）。实验结果表明，与最终结果的质量和目标函数的评估次数相比，该算法明显优于本文中其他最新的影响力最大化问题算法。 （C）2018 Elsevier B.V.保留所有权利。

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