改进多目标元启发式下的社会影响

Fabián Riquelme, Francisco Muñoz, Rodrigo Olivares
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引用次数: 1

摘要

影响最大化问题(IMP)和最小成本影响问题(LCI)是社会网络分析中两个相关且被广泛研究的问题。第一个包括最大化社交网络中的影响传播,从给定参与者的种子大小开始;第二种方法包括最小化种子集以达到给定数量的受影响节点。近年来,这两个问题被结合多目标元启发式方法进行了研究。在这项工作中,提出了基于网络拓扑的扩散滤波限制,以减少搜索空间,从而提高解的收敛速度。这个建议可以提高结果的质量。作为影响扩散模型,我们将使用线性阈值模型。该解决方案在三个不同规模的社交网络中进行了测试,在较困难的情况下发现了有希望的改进。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Social influence under improved multi-objective metaheuristics
The influence maximization problem (IMP) and the least cost influence problem (LCI) are two relevant and widely studied problems in social network analysis. The first one consists of maximizing the influence spread in a social network, starting with a given seed size of actors; the second one consists of minimizing the seed set to reach a given number of influenced nodes. Recently, both problems have been studied together with a multi-objective metaheuristic approach. In this work, diffusion filter restrictions based on the network topology are proposed to reduce the search space and thus improving the convergence speed of the solutions. This proposal allows increasing the quality of the results. As the influence spread model, the Linear Threshold model will be used. The solution is tested in three social networks of different sizes, finding promising improvements in harder instances.
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