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Dynamic patterns of information flow in complex networks

Author

Listed:
  • Uzi Harush

    (Bar-Ilan University)

  • Baruch Barzel

    (Bar-Ilan University)

Abstract

Although networks are extensively used to visualize information flow in biological, social and technological systems, translating topology into dynamic flow continues to challenge us, as similar networks exhibit fundamentally different flow patterns, driven by different interaction mechanisms. To uncover a network’s actual flow patterns, here we use a perturbative formalism, analytically tracking the contribution of all nodes/paths to the flow of information, exposing the rules that link structure and dynamic information flow for a broad range of nonlinear systems. We find that the diversity of flow patterns can be mapped into a single universal function, characterizing the interplay between the system’s topology and its dynamics, ultimately allowing us to identify the network’s main arteries of information flow. Counter-intuitively, our formalism predicts a family of frequently encountered dynamics where the flow of information avoids the hubs, favoring the network’s peripheral pathways, a striking disparity between structure and dynamics.

Suggested Citation

  • Uzi Harush & Baruch Barzel, 2017. "Dynamic patterns of information flow in complex networks," Nature Communications, Nature, vol. 8(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:8:y:2017:i:1:d:10.1038_s41467-017-01916-3
    DOI: 10.1038/s41467-017-01916-3
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    Cited by:

    1. van Elteren, Casper & Quax, Rick & Sloot, Peter, 2022. "Dynamic importance of network nodes is poorly predicted by static structural features," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 593(C).
    2. Jung, Hohyun & Phoa, Frederick Kin Hing, 2021. "On the effects of capability and popularity on network dynamics with applications to YouTube and Twitch networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 571(C).
    3. Li, Zhaoqing & Deng, Zhenghong & Han, Zhen & Alfaro-Bittner, Karin & Barzel, Baruch & Boccaletti, Stefano, 2021. "Contagion in simplicial complexes," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    4. Chen, Aimin & Wang, Pei & Zhou, Tianshou & Tian, Tianhai, 2022. "Balance of positive and negative regulation for trade-off between efficiency and resilience of high-dimensional networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 603(C).
    5. Alexander Rivkind & Hallel Schreier & Naama Brenner & Omri Barak, 2020. "Scale free topology as an effective feedback system," PLOS Computational Biology, Public Library of Science, vol. 16(5), pages 1-24, May.
    6. Duan, Dongli & Bai, Xue & Rong, Yisheng & Hou, Gege & Hang, Jiale, 2022. "Controlling of nonlinear dynamical networks based on decoupling and re-coupling method," Chaos, Solitons & Fractals, Elsevier, vol. 163(C).
    7. Sarah Gelper & Ralf van der Lans & Gerrit van Bruggen, 2021. "Competition for Attention in Online Social Networks: Implications for Seeding Strategies," Management Science, INFORMS, vol. 67(2), pages 1026-1047, February.
    8. Lv, Changchun & Yuan, Ziwei & Si, Shubin & Duan, Dongli, 2021. "Robustness of scale-free networks with dynamical behavior against multi-node perturbation," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).

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