IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v468y2017icp278-288.html
   My bibliography  Save this article

Identification of influential spreaders in online social networks using interaction weighted K-core decomposition method

Author

Listed:
  • Al-garadi, Mohammed Ali
  • Varathan, Kasturi Dewi
  • Ravana, Sri Devi

Abstract

Online social networks (OSNs) have become a vital part of everyday living. OSNs provide researchers and scientists with unique prospects to comprehend individuals on a scale and to analyze human behavioral patterns. Influential spreaders identification is an important subject in understanding the dynamics of information diffusion in OSNs. Targeting these influential spreaders is significant in planning the techniques for accelerating the propagation of information that is useful for various applications, such as viral marketing applications or blocking the diffusion of annoying information (spreading of viruses, rumors, online negative behaviors, and cyberbullying). Existing K-core decomposition methods consider links equally when calculating the influential spreaders for unweighted networks. Alternatively, the proposed link weights are based only on the degree of nodes. Thus, if a node is linked to high-degree nodes, then this node will receive high weight and is treated as an important node. Conversely, the degree of nodes in OSN context does not always provide accurate influence of users. In the present study, we improve the K-core method for OSNs by proposing a novel link-weighting method based on the interaction among users. The proposed method is based on the observation that the interaction of users is a significant factor in quantifying the spreading capability of user in OSNs. The tracking of diffusion links in the real spreading dynamics of information verifies the effectiveness of our proposed method for identifying influential spreaders in OSNs as compared with degree centrality, PageRank, and original K-core.

Suggested Citation

  • Al-garadi, Mohammed Ali & Varathan, Kasturi Dewi & Ravana, Sri Devi, 2017. "Identification of influential spreaders in online social networks using interaction weighted K-core decomposition method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 468(C), pages 278-288.
    • Handle: RePEc:eee:phsmap:v:468:y:2017:i:c:p:278-288
    DOI: 10.1016/j.physa.2016.11.002
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437116308068
    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.2016.11.002?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. Liu, Jian-Guo & Ren, Zhuo-Ming & Guo, Qiang, 2013. "Ranking the spreading influence in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(18), pages 4154-4159.
    2. Flaviano Morone & Hernán A. Makse, 2015. "Influence maximization in complex networks through optimal percolation," Nature, Nature, vol. 524(7563), pages 65-68, August.
    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. 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.
    5. Zhao, Laijun & Wang, Qin & Cheng, Jingjing & Chen, Yucheng & Wang, Jiajia & Huang, Wei, 2011. "Rumor spreading model with consideration of forgetting mechanism: A case of online blogging LiveJournal," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(13), pages 2619-2625.
    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. Yin, Likang & Deng, Yong, 2018. "Measuring transferring similarity via local information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 498(C), pages 102-115.
    2. Ma, Ning & Liu, Yijun & Chi, Yuxue, 2018. "Influencer discovery algorithm in a multi-relational network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 415-425.
    3. Li, Jiaman & Dong, Xiucheng & Jiang, Qingzhe & Dong, Kangyin & Liu, Guixian, 2021. "Natural gas trade network of countries and regions along the belt and road: Where to go in the future?," Resources Policy, Elsevier, vol. 71(C).
    4. Jiang, Cheng & Liu, Zhonghua, 2019. "Detecting multiple key players under the positive effect by using a distance-based connectivity approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    5. Miyoung Chong & Hae Jung Maria Kim, 2020. "Social roles and structural signatures of top influentials in the #prayforparis Twitter network," Quality & Quantity: International Journal of Methodology, Springer, vol. 54(1), pages 315-333, February.
    6. Tsugawa, Sho & Kimura, Kazuma, 2018. "Identifying influencers from sampled social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 507(C), pages 294-303.

    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. Namtirtha, Amrita & Dutta, Animesh & Dutta, Biswanath, 2018. "Identifying influential spreaders in complex networks based on kshell hybrid method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 499(C), pages 310-324.
    2. 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.
    3. Zhong, Lin-Feng & Shang, Ming-Sheng & Chen, Xiao-Long & Cai, Shi-Ming, 2018. "Identifying the influential nodes via eigen-centrality from the differences and similarities of structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 77-82.
    4. Bao, Zhong-Kui & Ma, Chuang & Xiang, Bing-Bing & Zhang, Hai-Feng, 2017. "Identification of influential nodes in complex networks: Method from spreading probability viewpoint," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 468(C), pages 391-397.
    5. Zhong, Lin-Feng & Liu, Quan-Hui & Wang, Wei & Cai, Shi-Min, 2018. "Comprehensive influence of local and global characteristics on identifying the influential nodes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 511(C), pages 78-84.
    6. 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.
    7. Fei, Liguo & Zhang, Qi & Deng, Yong, 2018. "Identifying influential nodes in complex networks based on the inverse-square law," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 1044-1059.
    8. Zhai, Li & Yan, Xiangbin & Zhang, Guojing, 2018. "Bi-directional h-index: A new measure of node centrality in weighted and directed networks," Journal of Informetrics, Elsevier, vol. 12(1), pages 299-314.
    9. 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.
    10. Fink, Christian G. & Fullin, Kelly & Gutierrez, Guillermo & Omodt, Nathan & Zinnecker, Sydney & Sprint, Gina & McCulloch, Sean, 2023. "A centrality measure for quantifying spread on weighted, directed networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 626(C).
    11. Yu, Senbin & Gao, Liang & Xu, Lida & Gao, Zi-You, 2019. "Identifying influential spreaders based on indirect spreading in neighborhood," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 418-425.
    12. 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).
    13. Yuan, Jia & Zhang, Qian-Ming & Gao, Jian & Zhang, Linyan & Wan, Xue-Song & Yu, Xiao-Jun & Zhou, Tao, 2016. "Promotion and resignation in employee networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 442-447.
    14. Xu, Shuqi & Mariani, Manuel Sebastian & Lü, Linyuan & Medo, Matúš, 2020. "Unbiased evaluation of ranking metrics reveals consistent performance in science and technology citation data," Journal of Informetrics, Elsevier, vol. 14(1).
    15. Huang, Wencheng & Li, Haoran & Yin, Yanhui & Zhang, Zhi & Xie, Anhao & Zhang, Yin & Cheng, Guo, 2024. "Node importance identification of unweighted urban rail transit network: An Adjacency Information Entropy based approach," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    16. 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.
    17. Wu, Yanlei & Yang, Yang & Jiang, Fei & Jin, Shuyuan & Xu, Jin, 2014. "Coritivity-based influence maximization in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 416(C), pages 467-480.
    18. Wang, Junyi & Hou, Xiaoni & Li, Kezan & Ding, Yong, 2017. "A novel weight neighborhood centrality algorithm for identifying influential spreaders in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 475(C), pages 88-105.
    19. Zhang, Dayong & Men, Hao & Zhang, Zhaoxin, 2024. "Assessing the stability of collaboration networks: A structural cohesion analysis perspective," Journal of Informetrics, Elsevier, vol. 18(1).
    20. Sun, Hong-liang & Chen, Duan-bing & He, Jia-lin & Ch’ng, Eugene, 2019. "A voting approach to uncover multiple influential spreaders on weighted networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 519(C), pages 303-312.

    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:468:y:2017:i:c:p:278-288. 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.