Hyper-aware adaptive heuristic algorithms: A novel approach for seed selection in hypergraph-based diffusion.
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| Názov: | Hyper-aware adaptive heuristic algorithms: A novel approach for seed selection in hypergraph-based diffusion. |
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| Autori: | Zhao D; School of Computer Science and Technology, Zhejiang Normal University, Jinhua, Zhejiang 321004, China., Zhang Y; School of Computer Science and Technology, Zhejiang Normal University, Jinhua, Zhejiang 321004, China., Zhang B; School of Cyber Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China., Qian C; School of Computer Science and Technology, Zhejiang Normal University, Jinhua, Zhejiang 321004, China., Zhong M; School of Computer Science and Technology, Zhejiang Normal University, Jinhua, Zhejiang 321004, China., Li S; School of Cyber Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China., Han J; School of Computer Science and Technology, Zhejiang Normal University, Jinhua, Zhejiang 321004, China., Peng H; School of Computer Science and Technology, Zhejiang Normal University, Jinhua, Zhejiang 321004, China.; Zhejiang Key Laboratory of Intelligent Education Technology and Application, Zhejiang Normal University, Jinhua, Zhejiang 321004, China., Wang W; School of Public Health, Chongqing Medical University, Chongqing 400016, China. |
| Zdroj: | Chaos (Woodbury, N.Y.) [Chaos] 2025 Nov 01; Vol. 35 (11). |
| Spôsob vydávania: | Journal Article |
| Jazyk: | English |
| Informácie o časopise: | Publisher: American Institute of Physics Country of Publication: United States NLM ID: 100971574 Publication Model: Print Cited Medium: Internet ISSN: 1089-7682 (Electronic) Linking ISSN: 10541500 NLM ISO Abbreviation: Chaos Subsets: PubMed not MEDLINE; MEDLINE |
| Imprint Name(s): | Publication: Melville, NY : American Institute of Physics Original Publication: Woodbury, NY : American Institute of Physics, 1991- |
| Abstrakt: | Influence maximization (IM) is a fundamental problem with broad applications in domains, such as social networks, information diffusion, and epidemic control. Traditional approaches predominantly model networks as ordinary graphs, which are limited in capturing complex higher-order group interactions. In contrast, hypergraphs provide a more natural and expressive representation of multi-node interactions. In this study, we investigate the IM problem on hypergraphs under the Susceptible-Infected spreading model with Contact Process dynamics. Leveraging node degree and hyperdegree, we propose four hyper-aware adaptive heuristic algorithms with distinct iterative update rules and systematically analyze the effects of incorporating the influence of selected seed nodes in first- and second-order neighbors on diffusion performance and computational efficiency. Extensive experiments on real-world and synthetic hypergraphs with varying degree heterogeneity demonstrate that the proposed algorithms consistently outperform baseline methods in terms of diffusion effectiveness, particularly under limited seed budgets, and exhibit strong robustness to variations in the hypergraph structure. Detailed analysis further reveals that the hyperdegree-scaled 1st-order neighbor reduction algorithm, which accounts for the influence of selected seeds in first-order neighbors, achieves an optimal trade-off between diffusion performance and computational efficiency, improving maximum effectiveness by 30.29% relative to baselines and reaching up to 63.42% improvement under constrained seed budgets. (© 2025 Author(s). Published under an exclusive license by AIP Publishing.) |
| Entry Date(s): | Date Created: 20251103 Latest Revision: 20251103 |
| Update Code: | 20251104 |
| DOI: | 10.1063/5.0297468 |
| PMID: | 41182145 |
| Databáza: | MEDLINE |
Nájsť tento článok vo Web of Science | Abstrakt: | Influence maximization (IM) is a fundamental problem with broad applications in domains, such as social networks, information diffusion, and epidemic control. Traditional approaches predominantly model networks as ordinary graphs, which are limited in capturing complex higher-order group interactions. In contrast, hypergraphs provide a more natural and expressive representation of multi-node interactions. In this study, we investigate the IM problem on hypergraphs under the Susceptible-Infected spreading model with Contact Process dynamics. Leveraging node degree and hyperdegree, we propose four hyper-aware adaptive heuristic algorithms with distinct iterative update rules and systematically analyze the effects of incorporating the influence of selected seed nodes in first- and second-order neighbors on diffusion performance and computational efficiency. Extensive experiments on real-world and synthetic hypergraphs with varying degree heterogeneity demonstrate that the proposed algorithms consistently outperform baseline methods in terms of diffusion effectiveness, particularly under limited seed budgets, and exhibit strong robustness to variations in the hypergraph structure. Detailed analysis further reveals that the hyperdegree-scaled 1st-order neighbor reduction algorithm, which accounts for the influence of selected seeds in first-order neighbors, achieves an optimal trade-off between diffusion performance and computational efficiency, improving maximum effectiveness by 30.29% relative to baselines and reaching up to 63.42% improvement under constrained seed budgets.<br /> (© 2025 Author(s). Published under an exclusive license by AIP Publishing.) |
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| ISSN: | 1089-7682 |
| DOI: | 10.1063/5.0297468 |