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Influence Maximization on Undirected Graphs: Toward Closing the (1 - 1/e) Gap

机译:在无向图的影响最大化:对关闭(1 - 1 / e)的差距

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摘要

We study the influence maximization problem in undirected networks, specifically focusing on the independent cascade and linear threshold models. We prove APX-hardness (NP-hardness of approximation within factor (1 - τ ) for some constant τ > 0) for both models, which improves the previous NP-hardness lower bound for the linear threshold model. No previous hardness result was known for the independent cascade model.
机译:我们研究了影响最大化问题无向网络,特别关注独立级联模型和线性阈值。我们证明APX-hardness (np困难的近似(1 -τ)对一些内部因素两种模型常数τ> 0),改善了前面的np困难的下界线性阈值模型。结果是著名的独立级联模型。

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