基于遺傳算法的低冗余超圖影響力最大化

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中圖分類號：TP301.5；N94 文獻(xiàn)標(biāo)識碼：A

Abstract： The influence maximization problem in hypergraphs has wide-ranging applications across various fields. Existing methods either inadequately address the redundancy of influence between nodes or only rely on a single metric for initial node ranking， which may fail to accurately capture the true propagation values of nodes. To simultaneously consider both influence redundancy between nodes and the actual propagation values of nodes，this paper proposes a Low Redundant Hypergraph based on the Genetic Algorithm （LR-HGA），which takes into account these two aspects in the selection and crossover operations of genetic algorithm. Experimental results on six real hypergraph networks using the SI propagation model defined on hypergraphs show that the seed set obtained by this algorithm generally has a wider influence spread compared to state-of-the-art benchmark algorithms.

Keywords： hypergraphs； influence maximization （IM）；influence redundancy； genetic algorithm（GA）

0 引言

由節(jié)點(diǎn)和鏈接組成的復(fù)雜網(wǎng)絡(luò)可以用來描述現(xiàn)實(shí)生活中的許多復(fù)雜系統(tǒng)，如蛋白質(zhì)網(wǎng)絡(luò)、交通網(wǎng)絡(luò)、生物網(wǎng)絡(luò)等。（剩余11725字）