Influence maximization independent of seed set size

IF 0.8 4区管理学 Q4 OPERATIONS RESEARCH & MANAGEMENT SCIENCE

Operations Research Letters Pub Date : 2025-05-23 DOI:10.1016/j.orl.2025.107309

K. Lakshmanan

引用次数: 0

Abstract

Recent work on clean runtime algorithms for the submodular function maximization problem suggests that the dependence of the seed set size for Influence Maximization (IM) algorithms may not be necessary. This also has practical significance if the seed set is of moderate size. We propose a simple modification to Reverse Influence Sampling (RIS) algorithm of Borgs et al. to make its runtime independent of the seed set size while giving the same

O (1 - 1 / e - ϵ)

-approximation guarantee.

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影响最大化与种子集大小无关

最近对子模函数最大化问题的干净运行时算法的研究表明，影响最大化（IM）算法的种子集大小的依赖性可能不是必要的。如果种子集大小适中，这也具有实际意义。我们对Borgs等人的反向影响采样（RIS）算法提出了一个简单的修改，使其运行时与种子集大小无关，同时给出相同的O(1−1/e−ε)-近似保证。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Operations Research Letters 管理科学-运筹学与管理科学

CiteScore

2.10

自引率

9.10%

发文量

111

审稿时长

83 days

期刊介绍： Operations Research Letters is committed to the rapid review and fast publication of short articles on all aspects of operations research and analytics. Apart from a limitation to eight journal pages, quality, originality, relevance and clarity are the only criteria for selecting the papers to be published. ORL covers the broad field of optimization, stochastic models and game theory. Specific areas of interest include networks, routing, location, queueing, scheduling, inventory, reliability, and financial engineering. We wish to explore interfaces with other fields such as life sciences and health care, artificial intelligence and machine learning, energy distribution, and computational social sciences and humanities. Our traditional strength is in methodology, including theory, modelling, algorithms and computational studies. We also welcome novel applications and concise literature reviews.