摘要翻译：
受病毒式营销等应用的推动，影响最大化(IM)问题在文献中得到了广泛的研究。目标是选择少量的用户来采用一个项目，这样它就会导致其他人大量采用。现有的工作有三个关键的限制。（1）它们不考虑用户在购买/采用物品时的经济考虑。（2）对多项目的研究主要集中在竞争上，对互补项目的研究很少。（3）对于网络拥有者来说，最大化社会福利对于保证顾客忠诚是很重要的，这在以前的IM文献中没有提到。在本文中，我们解决了这三个局限性，并提出了一个新的模型称为UIC，它将效用驱动的项目采纳与网络影响传播结合起来。在互补环境下，我们提出了在这种新环境下的社会福利最大化问题。我们证明，当目标函数既不是子模函数也不是超模函数时，令人惊讶的是，一个简单的贪婪分配算法获得了最优期望社会福利的因子$(1-1/e-ε)$。我们开发了该近似算法的可扩展版本\textsf{bundleGRD}，并在真实数据集和合成数据集上进行了全面的实验，证明该算法的性能明显优于所有基线。
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英文标题：
《Maximizing Welfare in Social Networks under a Utility Driven Influence
  Diffusion Model》
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作者：
Prithu Banerjee, Wei Chen and Laks V.S. Lakshmanan
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最新提交年份：
2019
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Social and Information Networks        社会和信息网络
分类描述：Covers the design, analysis, and modeling of social and information networks, including their applications for on-line information access, communication, and interaction, and their roles as datasets in the exploration of questions in these and other domains, including connections to the social and biological sciences. Analysis and modeling of such networks includes topics in ACM Subject classes F.2, G.2, G.3, H.2, and I.2; applications in computing include topics in H.3, H.4, and H.5; and applications at the interface of computing and other disciplines include topics in J.1--J.7. Papers on computer communication systems and network protocols (e.g. TCP/IP) are generally a closer fit to the Networking and Internet Architecture (cs.NI) category.
涵盖社会和信息网络的设计、分析和建模，包括它们在联机信息访问、通信和交互方面的应用，以及它们作为数据集在这些领域和其他领域的问题探索中的作用，包括与社会和生物科学的联系。这类网络的分析和建模包括ACM学科类F.2、G.2、G.3、H.2和I.2的主题；计算应用包括H.3、H.4和H.5中的主题；计算和其他学科接口的应用程序包括J.1-J.7中的主题。关于计算机通信系统和网络协议（例如TCP/IP）的论文通常更适合网络和因特网体系结构（CS.NI）类别。
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一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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英文摘要：
  Motivated by applications such as viral marketing, the problem of influence maximization (IM) has been extensively studied in the literature. The goal is to select a small number of users to adopt an item such that it results in a large cascade of adoptions by others. Existing works have three key limitations. (1) They do not account for economic considerations of a user in buying/adopting items. (2) Most studies on multiple items focus on competition, with complementary items receiving limited attention. (3) For the network owner, maximizing social welfare is important to ensure customer loyalty, which is not addressed in prior work in the IM literature. In this paper, we address all three limitations and propose a novel model called UIC that combines utility-driven item adoption with influence propagation over networks. Focusing on the mutually complementary setting, we formulate the problem of social welfare maximization in this novel setting. We show that while the objective function is neither submodular nor supermodular, surprisingly a simple greedy allocation algorithm achieves a factor of $(1-1/e-\epsilon)$ of the optimum expected social welfare. We develop \textsf{bundleGRD}, a scalable version of this approximation algorithm, and demonstrate, with comprehensive experiments on real and synthetic datasets, that it significantly outperforms all baselines.
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PDF链接：
https://arxiv.org/pdf/1807.02502