A multi-factorial evolutionary algorithm concerning diversity information for solving the multitasking Robust Influence Maximization Problem on networks

IF 3.2 4区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Connection Science Pub Date : 2023-11-16 DOI:10.1080/09540091.2023.2275534

Minghao Chen, Shuai Wang, Jiazhong Zhang

引用次数: 0

Abstract

In recent years, one of the prominent research areas in the complex network field has been the Influence Maximization Problem. This problem focuses on selecting seed sets to achieve optimal informa...

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基于多样性信息的多因子进化算法求解多任务鲁棒影响最大化问题

影响最大化问题是近年来复杂网络领域研究的热点之一。该问题的重点是选择种子集以获得最优信息。

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

Connection Science 工程技术-计算机：理论方法

CiteScore

6.50

自引率

39.60%

发文量

审稿时长

3 months

期刊介绍： Connection Science is an interdisciplinary journal dedicated to exploring the convergence of the analytic and synthetic sciences, including neuroscience, computational modelling, artificial intelligence, machine learning, deep learning, Database, Big Data, quantum computing, Blockchain, Zero-Knowledge, Internet of Things, Cybersecurity, and parallel and distributed computing. A strong focus is on the articles arising from connectionist, probabilistic, dynamical, or evolutionary approaches in aspects of Computer Science, applied applications, and systems-level computational subjects that seek to understand models in science and engineering.