Viral marketing campaigns are often negatively affected by overexposure. Overexposure occurs when users become less likely to favor a promoted product after receiving information about the product from too large a fraction of their friends. Yet, existing influence diffusion models do not take overexposure into account, effectively overestimating the number of users who favor the product and diffuse information about it. In this work, we propose the first influence diffusion model that captures overexposure. In our model, Latency Aware Independent Cascade Model with Overexposure (LAICO), the activation probability of a node representing a user is multiplied (discounted) by an overexposure score, which is calculated based on the ratio between the estimated and the maximum possible number of attempts performed to activate the node. We also study the influence maximization problem under LAICO. Since the spread function in LAICO is non-submodular, algorithms for submodular maximization are not appropriate to address the problem. Therefore, we develop an approximation algorithm that exploits monotone submodular upper and lower bound functions of spread, and a heuristic that aims to maximize a proxy function of spread iteratively. Our experiments show the effectiveness and efficiency of our algorithms.

中文翻译：

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过度曝光感知影响最大化

病毒式营销活动通常会受到过度曝光的负面影响。当用户从太多朋友那里收到有关产品的信息后，不太可能偏爱促销产品时，就会发生过度曝光。然而，现有的影响力扩散模型并未考虑过度曝光，从而有效地高估了喜欢该产品并传播有关该产品的信息的用户数量。在这项工作中，我们提出了第一个捕捉过度曝光的影响扩散模型。在我们的模型中，具有过度曝光的延迟感知独立级联模型 (LAICO)，代表用户的节点的激活概率乘以（折扣）过度曝光分数，该分数是根据估计的和最大可能数量之间的比率计算的尝试激活节点。我们还研究了 LAICO 下的影响最大化问题。由于 LAICO 中的扩展函数是非子模的，因此子模最大化算法不适用于解决该问题。因此，我们开发了一种近似算法，该算法利用单调子模上界和下界传播函数，以及旨在迭代地最大化传播代理函数的启发式算法。我们的实验证明了我们算法的有效性和效率。