Three-hop distance estimation in social graphs

Pascal Welke, Alexander Markowetz, Torsten Suel, Maria Christoforaki

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    In this paper, we study a 3-hop approach to distance estimation that uses two intermediate landmarks, where each landmark only stores distances to vertices in its local neighborhood and to the other landmarks. We show how to suitably represent and compress the distance data stored for each landmark, for the 2-hop and 3-hop case. Overall, we find that 3-hop methods achieve modest but promising improvement in some cases, while being comparable or slightly worse than 2-hop methods in others. Furthermore, our light compression schemes improve the practical applicability of both the 2-hop and 3-hop methods.

    Original languageEnglish (US)
    Title of host publicationProceedings - 2016 IEEE International Conference on Big Data, Big Data 2016
    EditorsRonay Ak, George Karypis, Yinglong Xia, Xiaohua Tony Hu, Philip S. Yu, James Joshi, Lyle Ungar, Ling Liu, Aki-Hiro Sato, Toyotaro Suzumura, Sudarsan Rachuri, Rama Govindaraju, Weijia Xu
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages1048-1055
    Number of pages8
    ISBN (Electronic)9781467390040
    DOIs
    StatePublished - Jan 1 2016
    Event4th IEEE International Conference on Big Data, Big Data 2016 - Washington, United States
    Duration: Dec 5 2016Dec 8 2016

    Publication series

    NameProceedings - 2016 IEEE International Conference on Big Data, Big Data 2016

    Other

    Other4th IEEE International Conference on Big Data, Big Data 2016
    CountryUnited States
    CityWashington
    Period12/5/1612/8/16

    Keywords

    • distance estimation
    • landmarks
    • shortest paths

    ASJC Scopus subject areas

    • Computer Networks and Communications
    • Information Systems
    • Hardware and Architecture

    Cite this

    Welke, P., Markowetz, A., Suel, T., & Christoforaki, M. (2016). Three-hop distance estimation in social graphs. In R. Ak, G. Karypis, Y. Xia, X. T. Hu, P. S. Yu, J. Joshi, L. Ungar, L. Liu, A-H. Sato, T. Suzumura, S. Rachuri, R. Govindaraju, & W. Xu (Eds.), Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016 (pp. 1048-1055). [7840706] (Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData.2016.7840706