IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v436y2015icp833-845.html
   My bibliography  Save this article

Improving detection of influential nodes in complex networks

Author

Listed:
  • Sheikhahmadi, Amir
  • Nematbakhsh, Mohammad Ali
  • Shokrollahi, Arman

Abstract

Recently an increasing amount of research is devoted to the question of how the most influential nodes (seeds) can be found effectively in a complex network. There are a number of measures proposed for this purpose, for instance, high-degree centrality measure reflects the importance of the network topology and has a reasonable runtime performance to find a set of nodes with highest degree, but they do not have a satisfactory dissemination potentiality in the network due to having many common neighbors (CN(1)) and common neighbors of neighbors (CN(2)). This flaw holds in other measures as well. In this paper, we compare high-degree centrality measure with other well-known measures using ten datasets in order to find a proportion for the common seeds in the seed sets obtained by them. We, thereof, propose an improved high-degree centrality measure (named DegreeDistance) and improve it to enhance accuracy in two phases, FIDD and SIDD, by put a threshold on the number of common neighbors of already-selected seed nodes and a non-seed node which is under investigation to be selected as a seed as well as considering the influence score of seed nodes directly or through their common neighbors over the non-seed node. To evaluate the accuracy and runtime performance of DegreeDistance, FIDD, and SIDD, they are applied to eight large-scale networks and it finally turns out that SIDD dramatically outperforms other well-known measures and evinces comparatively more accurate performance in identifying the most influential nodes.

Suggested Citation

  • Sheikhahmadi, Amir & Nematbakhsh, Mohammad Ali & Shokrollahi, Arman, 2015. "Improving detection of influential nodes in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 833-845.
    • Handle: RePEc:eee:phsmap:v:436:y:2015:i:c:p:833-845
    DOI: 10.1016/j.physa.2015.04.035
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437115004033
    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.2015.04.035?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. Raghuram Iyengar & Christophe Van den Bulte & Thomas W. Valente, 2011. "Opinion Leadership and Social Contagion in New Product Diffusion," Marketing Science, INFORMS, vol. 30(2), pages 195-212, 03-04.
    2. Gao, Cai & Wei, Daijun & Hu, Yong & Mahadevan, Sankaran & Deng, Yong, 2013. "A modified evidential methodology of identifying influential nodes in weighted networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(21), pages 5490-5500.
    3. Chen, Duanbing & Lü, Linyuan & Shang, Ming-Sheng & Zhang, Yi-Cheng & Zhou, Tao, 2012. "Identifying influential nodes in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 1777-1787.
    4. Yang, Jianmei & Yao, Canzhong & Ma, Weicheng & Chen, Guanrong, 2010. "A study of the spreading scheme for viral marketing based on a complex network model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(4), pages 859-870.
    5. Wei, Daijun & Deng, Xinyang & Zhang, Xiaoge & Deng, Yong & Mahadevan, Sankaran, 2013. "Identifying influential nodes in weighted networks based on evidence theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(10), pages 2564-2575.
    6. Hinz, Oliver & Schulze, Christian & Takac, Carsten, 2014. "New product adoption in social networks: Why direction matters," Journal of Business Research, Elsevier, vol. 67(1), pages 2836-2844.
    7. Gao, Shuai & Ma, Jun & Chen, Zhumin & Wang, Guanghui & Xing, Changming, 2014. "Ranking the spreading ability of nodes in complex networks based on local structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 403(C), pages 130-147.
    8. Liu, Jie & Li, Yunpeng & Ruan, Zichan & Fu, Guangyuan & Chen, Xiaowu & Sadiq, Rehan & Deng, Yong, 2015. "A new method to construct co-author networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 29-39.
    9. Florian Probst & Laura Grosswiele & Regina Pfleger, 2013. "Who will lead and who will follow: Identifying Influential Users in Online Social Networks," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 5(3), pages 179-193, June.
    10. Li, Qian & Zhou, Tao & Lü, Linyuan & Chen, Duanbing, 2014. "Identifying influential spreaders by weighted LeaderRank," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 404(C), pages 47-55.
    11. Leo Katz, 1953. "A new status index derived from sociometric analysis," Psychometrika, Springer;The Psychometric Society, vol. 18(1), pages 39-43, March.
    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. Sheikhahmadi, Amir & Nematbakhsh, Mohammad Ali & Zareie, Ahmad, 2017. "Identification of influential users by neighbors in online social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 517-534.
    2. Parand, Fereshteh-Azadi & Rahimi, Hossein & Gorzin, Mohsen, 2016. "Combining fuzzy logic and eigenvector centrality measure in social network analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 459(C), pages 24-31.
    3. Berahmand, Kamal & Bouyer, Asgarali & Samadi, Negin, 2018. "A new centrality measure based on the negative and positive effects of clustering coefficient for identifying influential spreaders in complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 110(C), pages 41-54.
    4. Zhu, Hengmin & Yin, Xicheng & Ma, Jing & Hu, Wei, 2016. "Identifying the main paths of information diffusion in online social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 452(C), pages 320-328.
    5. Maihami, Vafa & Yaghmaee, Farzin, 2018. "Automatic image annotation using community detection in neighbor images," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 507(C), pages 123-132.
    6. Jia, Jianlin & Chen, Yanyan & Wang, Yang & Li, Tongfei & Li, Yongxing, 2021. "A new global method for identifying urban rail transit key station during COVID-19: A case study of Beijing, China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    7. Zareie, Ahmad & Sheikhahmadi, Amir, 2019. "EHC: Extended H-index Centrality measure for identification of users’ spreading influence in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 141-155.
    8. Zhu, Hui & Wu, Heng & Cao, Jin & Fu, Gang & Li, Hui, 2018. "Information dissemination model for social media with constant updates," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 502(C), pages 469-482.
    9. Salavati, Chiman & Abdollahpouri, Alireza & Manbari, Zhaleh, 2018. "BridgeRank: A novel fast centrality measure based on local structure of the network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 496(C), pages 635-653.
    10. Wang, Min & Li, Wanchun & Guo, Yuning & Peng, Xiaoyan & Li, Yingxiang, 2020. "Identifying influential spreaders in complex networks based on improved k-shell method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 554(C).
    11. Zareie, Ahmad & Sheikhahmadi, Amir & Fatemi, Adel, 2017. "Influential nodes ranking in complex networks: An entropy-based approach," Chaos, Solitons & Fractals, Elsevier, vol. 104(C), pages 485-494.
    12. Wang, Xiaojie & Su, Yanyuan & Zhao, Chengli & Yi, Dongyun, 2016. "Effective identification of multiple influential spreaders by DegreePunishment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 238-247.

    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. Sheikhahmadi, Amir & Nematbakhsh, Mohammad Ali & Zareie, Ahmad, 2017. "Identification of influential users by neighbors in online social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 517-534.
    2. Wei, Bo & Liu, Jie & Wei, Daijun & Gao, Cai & Deng, Yong, 2015. "Weighted k-shell decomposition for complex networks based on potential edge weights," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 420(C), pages 277-283.
    3. Hu, Jiantao & Du, Yuxian & Mo, Hongming & Wei, Daijun & Deng, Yong, 2016. "A modified weighted TOPSIS to identify influential nodes in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 73-85.
    4. Berahmand, Kamal & Bouyer, Asgarali & Samadi, Negin, 2018. "A new centrality measure based on the negative and positive effects of clustering coefficient for identifying influential spreaders in complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 110(C), pages 41-54.
    5. Zhu, Hengmin & Yin, Xicheng & Ma, Jing & Hu, Wei, 2016. "Identifying the main paths of information diffusion in online social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 452(C), pages 320-328.
    6. Liu, Yang & Wei, Bo & Du, Yuxian & Xiao, Fuyuan & Deng, Yong, 2016. "Identifying influential spreaders by weight degree centrality in complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 86(C), pages 1-7.
    7. Chen, Yahong & Li, Jinlin & Huang, He & Ran, Lun & Hu, Yusheng, 2017. "Encouraging information sharing to boost the name-your-own-price auction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 479(C), pages 108-117.
    8. Gao, Shuai & Ma, Jun & Chen, Zhumin & Wang, Guanghui & Xing, Changming, 2014. "Ranking the spreading ability of nodes in complex networks based on local structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 403(C), pages 130-147.
    9. Miriam Däs & Julia Klier & Mathias Klier & Georg Lindner & Lea Thiel, 2017. "Customer lifetime network value: customer valuation in the context of network effects," Electronic Markets, Springer;IIM University of St. Gallen, vol. 27(4), pages 307-328, November.
    10. Zhang, Jun-li & Fu, Yan-jun & Cheng, Lan & Yang, Yun-yun, 2021. "Identifying multiple influential spreaders based on maximum connected component decomposition method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 571(C).
    11. Wang, Zhixiao & Zhao, Ya & Xi, Jingke & Du, Changjiang, 2016. "Fast ranking influential nodes in complex networks using a k-shell iteration factor," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 171-181.
    12. Salavati, Chiman & Abdollahpouri, Alireza & Manbari, Zhaleh, 2018. "BridgeRank: A novel fast centrality measure based on local structure of the network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 496(C), pages 635-653.
    13. Gao, Cai & Wei, Daijun & Hu, Yong & Mahadevan, Sankaran & Deng, Yong, 2013. "A modified evidential methodology of identifying influential nodes in weighted networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(21), pages 5490-5500.
    14. Hajarathaiah, Koduru & Enduri, Murali Krishna & Anamalamudi, Satish, 2022. "Efficient algorithm for finding the influential nodes using local relative change of average shortest path," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 591(C).
    15. Ni, Chengzhang & Yang, Jun & Kong, Demei, 2020. "Sequential seeding strategy for social influence diffusion with improved entropy-based centrality," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    16. Singh, Priti & Chakraborty, Abhishek & Manoj, B.S., 2017. "Link Influence Entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 701-713.
    17. Ma, Ling-ling & Ma, Chuang & Zhang, Hai-Feng & Wang, Bing-Hong, 2016. "Identifying influential spreaders in complex networks based on gravity formula," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 451(C), pages 205-212.
    18. Cai Gao & Xin Lan & Xiaoge Zhang & Yong Deng, 2013. "A Bio-Inspired Methodology of Identifying Influential Nodes in Complex Networks," PLOS ONE, Public Library of Science, vol. 8(6), pages 1-11, June.
    19. Ma, Qian & Ma, Jun, 2017. "Identifying and ranking influential spreaders in complex networks with consideration of spreading probability," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 312-330.
    20. Jansen, Nora & Hinz, Oliver, 2022. "Inferring opinion leadership from digital footprints," Journal of Business Research, Elsevier, vol. 139(C), pages 1123-1137.

    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:436:y:2015:i:c:p:833-845. 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.