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On Multi-Cascade Influence Maximization: Model, Hardness and Algorithmic Framework

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.