Probing the fitness landscape of the influential nodes for the influence maximization problem in social networks

IF 8.2 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Jianxin Tang, Jiaqiang Fu, Xinyue Li, Lele Geng, Juan Pang
{"title":"Probing the fitness landscape of the influential nodes for the influence maximization problem in social networks","authors":"Jianxin Tang,&nbsp;Jiaqiang Fu,&nbsp;Xinyue Li,&nbsp;Lele Geng,&nbsp;Juan Pang","doi":"10.1016/j.swevo.2025.102002","DOIUrl":null,"url":null,"abstract":"<div><div>Influence Maximization (IM) is a key issue of information dissemination and has been proved to be an NP-hard problem. However, traditional methods always suffer from low efficiency, poor scalability, and tend to fall into local optima. Probing the promising distribution regions of the potential influential nodes from the macroscopic perspective is necessary and helpful in understanding the influence propagation. To address such challenges, this paper makes attempt to depict the fitness landscape distribution of the expected influence of the social individuals in the network from a novel perspective. An entropy measure is introduced as a decision criterion and a fitness landscape-guided differential evolution optimization (FLDE) is proposed. Firstly, the distribution of the potential solution regions is depicted by characterizing the fitness landscape designed specially for IM problem. Next, a guiding strategy based on the fitness landscape is conceived to drive the differential evolution towards more promising solution regions by avoiding the entrapment in local optima. Experiments conducted on six real social networks and three synthetic networks indicate that the FLDE outperforms the state-of-the-art baselines by an average of 16% in influence spread and shows strong scalability when dealing with different types of networks.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"97 ","pages":"Article 102002"},"PeriodicalIF":8.2000,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Swarm and Evolutionary Computation","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2210650225001609","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

Abstract

Influence Maximization (IM) is a key issue of information dissemination and has been proved to be an NP-hard problem. However, traditional methods always suffer from low efficiency, poor scalability, and tend to fall into local optima. Probing the promising distribution regions of the potential influential nodes from the macroscopic perspective is necessary and helpful in understanding the influence propagation. To address such challenges, this paper makes attempt to depict the fitness landscape distribution of the expected influence of the social individuals in the network from a novel perspective. An entropy measure is introduced as a decision criterion and a fitness landscape-guided differential evolution optimization (FLDE) is proposed. Firstly, the distribution of the potential solution regions is depicted by characterizing the fitness landscape designed specially for IM problem. Next, a guiding strategy based on the fitness landscape is conceived to drive the differential evolution towards more promising solution regions by avoiding the entrapment in local optima. Experiments conducted on six real social networks and three synthetic networks indicate that the FLDE outperforms the state-of-the-art baselines by an average of 16% in influence spread and shows strong scalability when dealing with different types of networks.
探讨社交网络中影响最大化问题的影响节点的适应度景观
影响最大化是信息传播中的一个关键问题,已被证明是一个np困难问题。然而,传统方法存在效率低、可扩展性差、容易陷入局部最优的问题。从宏观的角度探索潜在影响节点的有希望分布区域,对于理解影响传播是必要的,也是有帮助的。针对这一挑战,本文试图从一个全新的视角来描绘社会个体在网络中预期影响的适应度景观分布。引入熵测度作为决策准则,提出了适应度景观引导下的差分进化优化算法(FLDE)。首先,通过描述专门针对IM问题设计的适应度景观来描述潜在解区域的分布。其次,提出了一种基于适应度景观的指导策略,通过避免陷入局部最优,推动差异进化向更有希望的解决方案区域发展。在6个真实社交网络和3个合成网络上进行的实验表明,FLDE在影响传播方面比最先进的基线平均高出16%,并且在处理不同类型的网络时表现出很强的可扩展性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Swarm and Evolutionary Computation
Swarm and Evolutionary Computation COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEC-COMPUTER SCIENCE, THEORY & METHODS
CiteScore
16.00
自引率
12.00%
发文量
169
期刊介绍: Swarm and Evolutionary Computation is a pioneering peer-reviewed journal focused on the latest research and advancements in nature-inspired intelligent computation using swarm and evolutionary algorithms. It covers theoretical, experimental, and practical aspects of these paradigms and their hybrids, promoting interdisciplinary research. The journal prioritizes the publication of high-quality, original articles that push the boundaries of evolutionary computation and swarm intelligence. Additionally, it welcomes survey papers on current topics and novel applications. Topics of interest include but are not limited to: Genetic Algorithms, and Genetic Programming, Evolution Strategies, and Evolutionary Programming, Differential Evolution, Artificial Immune Systems, Particle Swarms, Ant Colony, Bacterial Foraging, Artificial Bees, Fireflies Algorithm, Harmony Search, Artificial Life, Digital Organisms, Estimation of Distribution Algorithms, Stochastic Diffusion Search, Quantum Computing, Nano Computing, Membrane Computing, Human-centric Computing, Hybridization of Algorithms, Memetic Computing, Autonomic Computing, Self-organizing systems, Combinatorial, Discrete, Binary, Constrained, Multi-objective, Multi-modal, Dynamic, and Large-scale Optimization.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信