This paper studies the multi-cascade influence maximization problem, which explores the strategies for launching one information cascade in a social network in which multiple cascades can spread concurrently. We leverage the concept of resolution function for defining the correlations between different cascades and propose the independent multi-cascade model. Based on the proposed model, we study the multi-cascade influence maximization problem and provide approximation hardness under common complexity assumptions. Given the hardness results, we build a framework for designing seed selection algorithms with a data-dependent approximation ratio that can be computed in real-time. The main contribution is a novel sampling method for computing the upper and lower bounds that reveal the key combinatorial structure behind the multi-cascade influence maximization problem. The performance of the proposed algorithm is theoretically analyzed and practically evaluated through extensive simulations. The superiority of the proposed solution is supported by encouraging experimental results in terms of effectiveness and efficiency.

中文翻译：

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多级联影响最大化：模型、硬度和算法框架

本文研究了多级联影响最大化问题，探讨了在多个级联可以同时传播的社交网络中启动一个信息级联的策略。我们利用分辨率函数的概念来定义不同级联之间的相关性，并提出独立的多级联模型。基于所提出的模型，我们研究了多级联影响最大化问题并提供了常见复杂性假设下的近似硬度。鉴于硬度结果，我们构建了一个框架来设计种子选择算法，该算法具有可以实时计算的数据相关近似比。主要贡献是一种用于计算上下界的新颖采样方法，该方法揭示了多级联影响最大化问题背后的关键组合结构。通过广泛的模拟，对所提出算法的性能进行了理论分析和实际评估。所提出的解决方案的优越性得到了在有效性和效率方面令人鼓舞的实验结果的支持。