The bang for the buck: fair competitive viral marketing from the host perspective

Wei Lu, F. Bonchi, Amit Goyal, L. Lakshmanan
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引用次数: 80

Abstract

The key algorithmic problem in viral marketing is to identify a set of influential users (called seeds) in a social network, who, when convinced to adopt a product, shall influence other users in the network, leading to a large number of adoptions. When two or more players compete with similar products on the same network we talk about competitive viral marketing, which so far has been studied exclusively from the perspective of one of the competing players. In this paper we propose and study the novel problem of competitive viral marketing from the perspective of the host, i.e., the owner of the social network platform. The host sells viral marketing campaigns as a service to its customers, keeping control of the selection of seeds. Each company specifies its budget and the host allocates the seeds accordingly. From the host's perspective, it is important not only to choose the seeds to maximize the collective expected spread, but also to assign seeds to companies so that it guarantees the "bang for the buck" for all companies is nearly identical, which we formalize as the fair seed allocation problem. We propose a new propagation model capturing the competitive nature of viral marketing. Our model is intuitive and retains the desired properties of monotonicity and submodularity. We show that the fair seed allocation problem is NP-hard, and develop an efficient algorithm called Needy Greedy. We run experiments on three real-world social networks, showing that our algorithm is effective and scalable.
物有所值:从主机的角度来看,公平竞争的病毒式营销
病毒式营销的关键算法问题是在社交网络中识别一组有影响力的用户(称为种子),当他们被说服采用某种产品时,会影响网络中的其他用户,从而导致大量的采用。当两个或两个以上的玩家在同一网络上使用类似的产品竞争时,我们谈论的是竞争性病毒式营销,到目前为止,我们只从竞争对手之一的角度来研究这种营销。本文从宿主即社交网络平台所有者的角度出发,提出并研究了竞争性病毒式营销的新问题。主机将病毒式营销活动作为一种服务出售给客户,并保持对种子选择的控制。每个公司都指定了自己的预算,主持人根据预算分配种子。从主持人的角度来看,重要的是不仅要选择种子以最大化集体预期传播,而且要将种子分配给公司,以保证所有公司的“物有所值”几乎相同,我们将其正式化为公平的种子分配问题。我们提出了一种新的传播模型,捕捉病毒式营销的竞争本质。我们的模型是直观的,并且保留了期望的单调性和子模块性。我们证明了公平的种子分配问题是np困难的,并提出了一个有效的算法,称为需要贪婪。我们在三个现实世界的社交网络上进行了实验,表明我们的算法是有效的和可扩展的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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