Increasingly developing online social networks has enabled users to send or receive information very fast. However, due to the availability of an excessive amount of data in today’s society, managing the information has become very cumbersome, which may lead to the problem of information overload. This highly eminent problem, where the existence of too much relevant information available becomes a hindrance rather than a help, may cause losses, delays, and hardships in making decisions. Thus, in this paper, by defining information overload from a different aspect, we aim to maximize the information propagation while minimizing the information overload (duplication). To do so, we theoretically present the lower and upper bounds for the information overload using a bitwise-based approach as the leverage to mitigate the computation complexities and obtain an approximation ratio of . We propose two main algorithms, B-square and C-square, and compare them with the existing algorithms. Experiments on two types of datasets, synthetic and real-world networks, verify the effectiveness and efficiency of the proposed approach in addressing the problem.

中文翻译：

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影响力过载问题的按位方法

日益发展的在线社交网络使用户能够非常快速地发送或接收信息。然而，由于当今社会的数据量过多，管理信息变得非常繁琐，这可能会导致信息过载的问题。这个非常突出的问题是，过多的相关信息的存在会成为一种障碍而不是帮助，可能会导致决策过程中的损失、延误和困难。因此，在本文中，通过从不同的方面定义信息过载，我们的目标是最大化信息传播，同时最小化信息过载（重复）。为此，我们从理论上提出了信息过载的下限和上限，使用基于位的方法作为减轻计算复杂性并获得近似比的杠杆。我们提出了两种主要算法，B-square和C-square，并将它们与现有算法进行了比较。对两种类型的数据集（合成网络和真实世界网络）的实验验证了所提出的方法在解决问题方面的有效性和效率。