IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v586y2022ics0378437121007536.html
   My bibliography  Save this article

An improved influence maximization method for social networks based on genetic algorithm

Author

Listed:
  • Jabari Lotf, Jalil
  • Abdollahi Azgomi, Mohammad
  • Ebrahimi Dishabi, Mohammad Reza

Abstract

Over the recent decade, much research has been conducted in the field of social networks. The structure of these networks has been irregular, complex, and dynamic, and certain challenges such as network topology, scalability, and high computational complexities are typically evident. Because of the changes in the structure of social networks over time and the widespread diffusion of ideas, seed sets also need to change over time. Since there have been limited studies on highly dynamical changes in real networks, this research intended to address the network dynamicity in the classical influence maximization problem, which discovers a small subset of nodes in a social network and maximizes the influence spread. To this end, we used soft computing methods (i.e., a dynamic generalized genetic algorithm) in social networks under independent cascade models to obtain a dynamic seed set. We modeled several graphs in a specified timestamp through which the edges and the nodes changed within different time intervals. Attempts were made to find influential individuals in each of these graphs and maximize individuals’ influences in social networks, which could thereby lead to changes in the members of the seed set. The proposed method was evaluated using standard datasets. The results showed that due to the reduction of the search areas and competition, the proposed method has higher scalability and accuracy to identify influential nodes in these snapshot graphs as compared with other comparable algorithms.

Suggested Citation

  • Jabari Lotf, Jalil & Abdollahi Azgomi, Mohammad & Ebrahimi Dishabi, Mohammad Reza, 2022. "An improved influence maximization method for social networks based on genetic algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 586(C).
    • Handle: RePEc:eee:phsmap:v:586:y:2022:i:c:s0378437121007536
    DOI: 10.1016/j.physa.2021.126480
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437121007536
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2021.126480?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Wang, Yuejiao & Zhang, Yatao & Yang, Fei & Li, Dong & Sun, Xin & Ma, Jun, 2021. "Time-sensitive Positive Influence Maximization in signed social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 584(C).
    2. Ullah, Farman & Lee, Sungchang, 2017. "Identification of influential nodes based on temporal-aware modeling of multi-hop neighbor interactions for influence spread maximization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 968-985.
    3. Andrea Landherr & Bettina Friedl & Julia Heidemann, 2010. "A Critical Review of Centrality Measures in Social Networks," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 2(6), pages 371-385, December.
    4. Aghaalizadeh, Saeid & Afshord, Saeid Taghavi & Bouyer, Asgarali & Anari, Babak, 2021. "A three-stage algorithm for local community detection based on the high node importance ranking in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 563(C).
    5. Can, Umit & Alatas, Bilal, 2019. "A new direction in social network analysis: Online social network analysis problems and applications," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    6. Zhang, Kaiqi & Du, Haifeng & Feldman, Marcus W., 2017. "Maximizing influence in a social network: Improved results using a genetic algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 478(C), pages 20-30.
    7. Dariush Khezrimotlagh & Yao Chen, 2018. "The Optimization Approach," International Series in Operations Research & Management Science, in: Decision Making and Performance Evaluation Using Data Envelopment Analysis, chapter 0, pages 107-134, Springer.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Tao, Li & Kong, Shengzhou & He, Langzhou & Zhang, Fan & Li, Xianghua & Jia, Tao & Han, Zhen, 2022. "A sequential-path tree-based centrality for identifying influential spreaders in temporal networks," Chaos, Solitons & Fractals, Elsevier, vol. 165(P1).
    2. Ricardo S. Santos & Jose Soares & Pedro Carmona Marques & Helena V. G. Navas & José Moleiro Martins, 2021. "Integrating Business, Social, and Environmental Goals in Open Innovation through Partner Selection," Sustainability, MDPI, vol. 13(22), pages 1-25, November.
    3. Zhang, Xian-Jie & Wang, Jing & Ma, Xiao-Jing & Ma, Chuang & Kan, Jia-Qian & Zhang, Hai-Feng, 2022. "Influence maximization in social networks with privacy protection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wang, Yongli & Wang, Yudong & Huang, Yujing & Yang, Jiale & Ma, Yuze & Yu, Haiyang & Zeng, Ming & Zhang, Fuwei & Zhang, Yanfu, 2019. "Operation optimization of regional integrated energy system based on the modeling of electricity-thermal-natural gas network," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    2. Yang, Lin & Pang, Shujiang & Wang, Xiaoyan & Du, Yi & Huang, Jieyu & Melching, Charles S., 2021. "Optimal allocation of best management practices based on receiving water capacity constraints," Agricultural Water Management, Elsevier, vol. 258(C).
    3. Xu, Xiangdong & Qu, Kai & Chen, Anthony & Yang, Chao, 2021. "A new day-to-day dynamic network vulnerability analysis approach with Weibit-based route adjustment process," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 153(C).
    4. Wang, Yongli & Li, Jiapu & Wang, Shuo & Yang, Jiale & Qi, Chengyuan & Guo, Hongzhen & Liu, Ximei & Zhang, Hongqing, 2020. "Operational optimization of wastewater reuse integrated energy system," Energy, Elsevier, vol. 200(C).
    5. Changyu Zhou & Guohe Huang & Jiapei Chen, 2019. "A Type-2 Fuzzy Chance-Constrained Fractional Integrated Modeling Method for Energy System Management of Uncertainties and Risks," Energies, MDPI, vol. 12(13), pages 1-21, June.
    6. Hu, Lin & Hu, Xiaosong & Che, Yunhong & Feng, Fei & Lin, Xianke & Zhang, Zhiyong, 2020. "Reliable state of charge estimation of battery packs using fuzzy adaptive federated filtering," Applied Energy, Elsevier, vol. 262(C).
    7. Hao, Ran & Lu, Tianguang & Ai, Qian & Wang, Zhe & Wang, Xiaolong, 2020. "Distributed online learning and dynamic robust standby dispatch for networked microgrids," Applied Energy, Elsevier, vol. 274(C).
    8. Kazemzadeh, Farzaneh & Safaei, Ali Asghar & Mirzarezaee, Mitra, 2022. "Influence maximization in social networks using effective community detection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 598(C).
    9. Li, Yang & Wang, Bin & Yang, Zhen & Li, Jiazheng & Chen, Chen, 2022. "Hierarchical stochastic scheduling of multi-community integrated energy systems in uncertain environments via Stackelberg game," Applied Energy, Elsevier, vol. 308(C).
    10. Qiaohua Fang & Xuezhe Wei & Haifeng Dai, 2019. "A Remaining Discharge Energy Prediction Method for Lithium-Ion Battery Pack Considering SOC and Parameter Inconsistency," Energies, MDPI, vol. 12(6), pages 1-24, March.
    11. Ming Zhang & Qianwen Huang & Sihan Liu & Huiying Li, 2019. "Multi-Objective Optimization of Aircraft Taxiing on the Airport Surface with Consideration to Taxiing Conflicts and the Airport Environment," Sustainability, MDPI, vol. 11(23), pages 1-27, November.
    12. Ruidi Chen & Ioannis Ch. Paschalidis, 2022. "Robust Grouped Variable Selection Using Distributionally Robust Optimization," Journal of Optimization Theory and Applications, Springer, vol. 194(3), pages 1042-1071, September.
    13. Darya Pyatkina & Tamara Shcherbina & Vadim Samusenkov & Irina Razinkina & Mariusz Sroka, 2021. "Modeling and Management of Power Supply Enterprises’ Cash Flows," Energies, MDPI, vol. 14(4), pages 1-17, February.
    14. Kumar Jadoun, Vinay & Rahul Prashanth, G & Suhas Joshi, Siddharth & Narayanan, K. & Malik, Hasmat & García Márquez, Fausto Pedro, 2022. "Optimal fuzzy based economic emission dispatch of combined heat and power units using dynamically controlled Whale Optimization Algorithm," Applied Energy, Elsevier, vol. 315(C).
    15. Mohammed Abdullah H. Alshehri & Youguang Guo & Gang Lei, 2023. "Energy Management Strategies of Grid-Connected Microgrids under Different Reliability Conditions," Energies, MDPI, vol. 16(9), pages 1-22, May.
    16. Ye, Rui-Ke & Gao, Zhuang-Fei & Fang, Kai & Liu, Kang-Li & Chen, Jia-Wei, 2021. "Moving from subsidy stimulation to endogenous development: A system dynamics analysis of China's NEVs in the post-subsidy era," Technological Forecasting and Social Change, Elsevier, vol. 168(C).
    17. Fink, Kilian & Krüger, Ulrich & Meller, Barbara & Wong, Lui-Hsian, 2016. "The credit quality channel: Modeling contagion in the interbank market," Journal of Financial Stability, Elsevier, vol. 25(C), pages 83-97.
    18. Meng, Jinhao & Cai, Lei & Stroe, Daniel-Ioan & Luo, Guangzhao & Sui, Xin & Teodorescu, Remus, 2019. "Lithium-ion battery state-of-health estimation in electric vehicle using optimized partial charging voltage profiles," Energy, Elsevier, vol. 185(C), pages 1054-1062.
    19. Theodoros Kalogiannis & Md Sazzad Hosen & Mohsen Akbarzadeh Sokkeh & Shovon Goutam & Joris Jaguemont & Lu Jin & Geng Qiao & Maitane Berecibar & Joeri Van Mierlo, 2019. "Comparative Study on Parameter Identification Methods for Dual-Polarization Lithium-Ion Equivalent Circuit Model," Energies, MDPI, vol. 12(21), pages 1-35, October.
    20. Izzet Alp Gul & Gülgün Kayakutlu & M. Özgür Kayalica, 2020. "Risk Analysis in Renewable Energy System (RES) Investment for a Developing Country: A Case Study in Pakistan," Arthaniti: Journal of Economic Theory and Practice, , vol. 19(2), pages 204-223, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:586:y:2022:i:c:s0378437121007536. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.