An evaluation tool for backbone extraction techniques in weighted complex networks

被引:4
作者
Yassin, Ali [1 ]
Haidar, Abbas [2 ]
Cherifi, Hocine [3 ]
Seba, Hamida [4 ]
Togni, Olivier [1 ]
机构
[1] Univ Burgundy, Lab Informat Bourgogne, Dijon, France
[2] Lebanese Univ, Comp Sci Dept, Beirut, Lebanon
[3] Univ Bourgogne Franche Comte, CNRS, UMR 6303, ICB, Dijon, France
[4] Univ Lyon, CNRS, UMR 5205, INSA Lyon,UCBL, F-69622 Villeurbanne, France
关键词
MULTISCALE BACKBONE; VISUALIZATION; SCIENCE; !text type='PYTHON']PYTHON[!/text; SCALE;
D O I
10.1038/s41598-023-42076-3
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Networks are essential for analyzing complex systems. However, their growing size necessitates backbone extraction techniques aimed at reducing their size while retaining critical features. In practice, selecting, implementing, and evaluating the most suitable backbone extraction method may be challenging. This paper introduces netbone, a Python package designed for assessing the performance of backbone extraction techniques in weighted networks. Its comparison framework is the standout feature of netbone. Indeed, the tool incorporates state-of-the-art backbone extraction techniques. Furthermore, it provides a comprehensive suite of evaluation metrics allowing users to evaluate different backbones techniques. We illustrate the flexibility and effectiveness of netbone through the US air transportation network analysis. We compare the performance of different backbone extraction techniques using the evaluation metrics. We also show how users can integrate a new backbone extraction method into the comparison framework. netbone is publicly available as an open-source tool, ensuring its accessibility to researchers and practitioners. Promoting standardized evaluation practices contributes to the advancement of backbone extraction techniques and fosters reproducibility and comparability in research efforts. We anticipate that netbone will serve as a valuable resource for researchers and practitioners enabling them to make informed decisions when selecting backbone extraction techniques to gain insights into the structural and functional properties of complex systems.
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页数:19
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共 125 条
  • [1] Flavor network and the principles of food pairing
    Ahn, Yong-Yeol
    Ahnert, Sebastian E.
    Bagrow, James P.
    Barabasi, Albert-Laszlo
    [J]. SCIENTIFIC REPORTS, 2011, 1
  • [2] Alanis-Lobato G., 2017, A reliable and unbiased human protein network with the disparity filter, DOI [10.1101/207761, DOI 10.1101/207761]
  • [3] Statistical mechanics of complex networks
    Albert, R
    Barabási, AL
    [J]. REVIEWS OF MODERN PHYSICS, 2002, 74 (01) : 47 - 97
  • [4] Metabolic plasticity in synthetic lethal mutants: Viability at higher cost
    Alessandro Massucci, Francesco
    Sagues, Francesc
    Angeles Serrano, M.
    [J]. PLOS COMPUTATIONAL BIOLOGY, 2018, 14 (01)
  • [5] powerlaw: A Python']Python Package for Analysis of Heavy-Tailed Distributions
    Alstott, Jeff
    Bullmore, Edward T.
    Plenz, Dietmar
    [J]. PLOS ONE, 2014, 9 (01):
  • [6] Opinion competition dynamics on multiplex networks
    Amato, R.
    Kouvaris, N. E.
    San Miguel, M.
    Diaz-Guilera, A.
    [J]. NEW JOURNAL OF PHYSICS, 2017, 19
  • [7] Uncovering the hidden geometry behind metabolic networks
    Angeles Serrano, M.
    Boguna, Marian
    Sagues, Francesc
    [J]. MOLECULAR BIOSYSTEMS, 2012, 8 (03) : 843 - 850
  • [8] Detecting coalitions by optimally partitioning signed networks of political collaboration
    Aref, Samin
    Neal, Zachary
    [J]. SCIENTIFIC REPORTS, 2020, 10 (01)
  • [9] Auber D, 2004, MATH VIS, P105
  • [10] Motif-h: a novel functional backbone extraction for directed networks
    Bai, Yiguang
    Li, Qian
    Fan, Yanni
    Liu, Sanyang
    [J]. COMPLEX & INTELLIGENT SYSTEMS, 2021, 7 (06) : 3277 - 3287