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, Jiaqiang Fu, Xinyue Li, Lele Geng, 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.
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.