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Influence Propagation in Competitive Scenario: Winning with Least Amount of Investment in Separated Threshold Model

机译:在竞争场景中的影响传播:在分离阈值模型中以最少的投资获胜

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In a market environment, there often are multiple vendors offering similar products or services. It has been observed that individuals' decisions to adopt a product or service are influenced by the recommendations of their friends and acquaintances. Consequently, in the last few years there has been considerable interest in the research community to study the dynamics of influence propagation in social networks in competitive settings. The goal of these studies is often to identify the key individuals in a social network, whose recommendations have significant impact on adoption of a product or service by the members of that community. Using Separated Threshold Model (SepT) [1] of influence propagation, in this paper we study a problem of similar vein, where the goal of the two vendors (players) is to win the competition by having a market share that is larger than its competitor. In our model, the first player has already identified a set of key influencers when the second player enters the market. The goal of the second player is to have a larger market share, but wants to achieve the goal with least amount of investment, i.e., by incentivizing the fewest number of key individuals (influencers) in the social network. The problem is NP-hard. We provide an approximation algorithm with O(log n) bound. Detailed experimentations have been conducted to evaluate the efficacy of our algorithm. Moreover, we present an equivalent random process for the SepT model which facilitates analysis of competitive influence propagation under this model.
机译:在市场环境中,经常有多个供应商提供相似的产品或服务。已经观察到,个人采用产品或服务的决定受其朋友和熟人推荐的影响。因此,在过去的几年中,研究界对研究竞争环境中社交网络中影响力传播的动力学产生了浓厚的兴趣。这些研究的目的通常是确定社交网络中的关键人物,他们的建议对该社区成员对产品或服务的采用产生重大影响。本文使用影响力传播的分离阈值模型(SepT)[1],研究了一个类似的问题,即两个供应商(参与者)的目标是通过拥有比其更大的市场份额来赢得竞争。竞争者。在我们的模型中,当第二个参与者进入市场时,第一个参与者已经确定了一组关键影响者。第二个参与者的目标是拥有更大的市场份额,但希望以最少的投资来实现该目标,即通过激励社交网络中最少数量的关键人物(影响者)。问题是NP困难的。我们提供了一个以O(log n)为界的近似算法。已经进行了详细的实验以评估我们算法的有效性。此外,我们为SepT模型提出了一个等效的随机过程,该过程有助于分析该模型下竞争影响的传播。

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