基于遗传算法的低冗余超图影响力最大化

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中图分类号：TP301.5；N94 文献标识码：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

由节点和链接组成的复杂网络可以用来描述现实生活中的许多复杂系统，如蛋白质网络、交通网络、生物网络等。（剩余11725字）