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Robust classification of salient links in complex networks

Author

Listed:
  • Daniel Grady

    (Northwestern University)

  • Christian Thiemann

    (Northwestern University
    Max-Planck-Institut für Dynamik und Selbstorganisation)

  • Dirk Brockmann

    (Northwestern University
    Northwestern Institute on Complex Systems, Northwestern University)

Abstract

Complex networks in natural, social and technological systems generically exhibit an abundance of rich information. Extracting meaningful structural features from data is one of the most challenging tasks in network theory. Many methods and concepts have been proposed to address this problem such as centrality statistics, motifs, community clusters and backbones, but such schemes typically rely on external and arbitrary parameters. It is unknown whether generic networks permit the classification of elements without external intervention. Here we show that link salience is a robust approach to classifying network elements based on a consensus estimate of all nodes. A wide range of empirical networks exhibit a natural, network-implicit classification of links into qualitatively distinct groups, and the salient skeletons have generic statistical properties. Salience also predicts essential features of contagion phenomena on networks, and points towards a better understanding of universal features in empirical networks that are masked by their complexity.

Suggested Citation

  • Daniel Grady & Christian Thiemann & Dirk Brockmann, 2012. "Robust classification of salient links in complex networks," Nature Communications, Nature, vol. 3(1), pages 1-10, January.
    • Handle: RePEc:nat:natcom:v:3:y:2012:i:1:d:10.1038_ncomms1847
    DOI: 10.1038/ncomms1847
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    Cited by:

    1. Malang, Kanokwan & Wang, Shuliang & Phaphuangwittayakul, Aniwat & Lv, Yuanyuan & Yuan, Hanning & Zhang, Xiuzhen, 2020. "Identifying influential nodes of global terrorism network: A comparison for skeleton network extraction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    2. Pagnottoni, Paolo & Spelta, Alessandro, 2023. "The motifs of risk transmission in multivariate time series: Application to commodity prices," Socio-Economic Planning Sciences, Elsevier, vol. 87(PB).
    3. Wu, Tao & Chen, Leiting & Zhong, Linfeng & Xian, Xingping, 2017. "Predicting the evolution of complex networks via similarity dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 662-672.
    4. Lu, Xin & Horn, Abigail L. & Su, Jiahao & Jiang, Jiang, 2019. "A Universal Measure for Network Traceability," Omega, Elsevier, vol. 87(C), pages 191-204.
    5. Nimrod Serok & Orr Levy & Shlomo Havlin & Efrat Blumenfeld-Lieberthal, 2019. "Unveiling the inter-relations between the urban streets network and its dynamic traffic flows: Planning implication," Environment and Planning B, , vol. 46(7), pages 1362-1376, September.
    6. Lordan, Oriol & Sallan, Jose M. & Simo, Pep, 2014. "Study of the topology and robustness of airline route networks from the complex network approach: a survey and research agenda," Journal of Transport Geography, Elsevier, vol. 37(C), pages 112-120.
    7. Viljoen, Nadia M. & Joubert, Johan W., 2016. "The vulnerability of the global container shipping network to targeted link disruption," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 396-409.
    8. Yang, Ming-Yuan & Wu, Zhen-Guo & Wu, Xin & Li, Sai-Ping, 2024. "Influential risk spreaders and systemic risk in Chinese financial networks," Emerging Markets Review, Elsevier, vol. 60(C).

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