IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0034740.html
   My bibliography  Save this article

One Plus One Makes Three (for Social Networks)

Author

Listed:
  • Emöke-Ágnes Horvát
  • Michael Hanselmann
  • Fred A Hamprecht
  • Katharina A Zweig

Abstract

Members of social network platforms often choose to reveal private information, and thus sacrifice some of their privacy, in exchange for the manifold opportunities and amenities offered by such platforms. In this article, we show that the seemingly innocuous combination of knowledge of confirmed contacts between members on the one hand and their email contacts to non-members on the other hand provides enough information to deduce a substantial proportion of relationships between non-members. Using machine learning we achieve an area under the (receiver operating characteristic) curve () of at least for predicting whether two non-members known by the same member are connected or not, even for conservative estimates of the overall proportion of members, and the proportion of members disclosing their contacts.

Suggested Citation

  • Emöke-Ágnes Horvát & Michael Hanselmann & Fred A Hamprecht & Katharina A Zweig, 2012. "One Plus One Makes Three (for Social Networks)," PLOS ONE, Public Library of Science, vol. 7(4), pages 1-8, April.
  • Handle: RePEc:plo:pone00:0034740
    DOI: 10.1371/journal.pone.0034740
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0034740
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0034740&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0034740?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
    ---><---

    References listed on IDEAS

    as
    1. David Liben‐Nowell & Jon Kleinberg, 2007. "The link‐prediction problem for social networks," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 58(7), pages 1019-1031, May.
    2. Aaron Clauset & Cristopher Moore & M. E. J. Newman, 2008. "Hierarchical structure and the prediction of missing links in networks," Nature, Nature, vol. 453(7191), pages 98-101, May.
    3. Tao Zhou & Linyuan Lü & Yi-Cheng Zhang, 2009. "Predicting missing links via local information," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 71(4), pages 623-630, October.
    4. Sid Redner, 2008. "Teasing out the missing links," Nature, Nature, vol. 453(7191), pages 47-48, May.
    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. Zexun Chen & Sean Kelty & Alexandre G. Evsukoff & Brooke Foucault Welles & James Bagrow & Ronaldo Menezes & Gourab Ghoshal, 2022. "Contrasting social and non-social sources of predictability in human mobility," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    2. Sætra, Henrik Skaug, 2019. "Freedom under the gaze of Big Brother: Preparing the grounds for a liberal defence of privacy in the era of Big Data," Technology in Society, Elsevier, vol. 58(C).
    3. Sætra, Henrik Skaug, 2020. "Privacy as an aggregate public good," Technology in Society, Elsevier, vol. 63(C).
    4. Philipp K. Masur, 2020. "How Online Privacy Literacy Supports Self-Data Protection and Self-Determination in the Age of Information," Media and Communication, Cogitatio Press, vol. 8(2), pages 258-269.

    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. Lee, Yan-Li & Zhou, Tao, 2021. "Collaborative filtering approach to link prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 578(C).
    2. Lü, Linyuan & Zhou, Tao, 2011. "Link prediction in complex networks: A survey," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(6), pages 1150-1170.
    3. Zhou, Tao & Lee, Yan-Li & Wang, Guannan, 2021. "Experimental analyses on 2-hop-based and 3-hop-based link prediction algorithms," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 564(C).
    4. Ankita Singh & Nanhay Singh, 2022. "An approach for predicting missing links in social network using node attribute and path information," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(2), pages 944-956, April.
    5. Bütün, Ertan & Kaya, Mehmet, 2019. "A pattern based supervised link prediction in directed complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 1136-1145.
    6. Yueran Duan & Qing Guan, 2021. "Predicting potential knowledge convergence of solar energy: bibliometric analysis based on link prediction model," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(5), pages 3749-3773, May.
    7. Mingyu Nan & Yifan Zhu & Jie Zhang & Tao Wang & Xin Zhou, 2022. "MSGWO-MKL-SVM: A Missing Link Prediction Method for UAV Swarm Network Based on Time Series," Mathematics, MDPI, vol. 10(14), pages 1-29, July.
    8. Park, Ji Hwan & Chang, Woojin & Song, Jae Wook, 2020. "Link prediction in the Granger causality network of the global currency market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 553(C).
    9. Lee, Yan-Li & Dong, Qiang & Zhou, Tao, 2021. "Link prediction via controlling the leading eigenvector," Applied Mathematics and Computation, Elsevier, vol. 411(C).
    10. Jiao, Yang & Wu, Jianshe & Xiang, Peng & Wang, Fang, 2023. "Link prediction from fusion information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 618(C).
    11. Wang, Jun & Zhang, Qian-Ming & Zhou, Tao, 2019. "Tag-aware link prediction algorithm in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 105-111.
    12. Liao, Hao & Zeng, An & Zhang, Yi-Cheng, 2015. "Predicting missing links via correlation between nodes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 216-223.
    13. Huang, Lu & Chen, Xiang & Ni, Xingxing & Liu, Jiarun & Cao, Xiaoli & Wang, Changtian, 2021. "Tracking the dynamics of co-word networks for emerging topic identification," Technological Forecasting and Social Change, Elsevier, vol. 170(C).
    14. Chen, Xing & Wu, Tao & Xian, Xingping & Wang, Chao & Yuan, Ye & Ming, Guannan, 2020. "Enhancing robustness of link prediction for noisy complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 555(C).
    15. Zhou, Tao, 2023. "Discriminating abilities of threshold-free evaluation metrics in link prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 615(C).
    16. Lingling Zhang & Jing Li & Qiuliu Zhang & Fan Meng & Weili Teng, 2019. "Domain Knowledge-Based Link Prediction in Customer-Product Bipartite Graph for Product Recommendation," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(01), pages 311-338, January.
    17. Chunning Wang & Fengqin Tang & Xuejing Zhao, 2023. "LPGRI: A Global Relevance-Based Link Prediction Approach for Multiplex Networks," Mathematics, MDPI, vol. 11(14), pages 1-15, July.
    18. Tofighy, Sajjad & Charkari, Nasrollah Moghadam & Ghaderi, Foad, 2022. "Link prediction in multiplex networks using intralayer probabilistic distance and interlayer co-evolving factors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 606(C).
    19. Lv, Laishui & Bardou, Dalal & Hu, Peng & Liu, Yanqiu & Yu, Gaohang, 2022. "Graph regularized nonnegative matrix factorization for link prediction in directed temporal networks using PageRank centrality," Chaos, Solitons & Fractals, Elsevier, vol. 159(C).
    20. Karimi, Fatemeh & Lotfi, Shahriar & Izadkhah, Habib, 2021. "Community-guided link prediction in multiplex networks," Journal of Informetrics, Elsevier, vol. 15(4).

    More about this item

    Statistics

    Access and download statistics

    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:plo:pone00:0034740. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.