IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v13y2022i1d10.1038_s41467-022-34267-9.html
   My bibliography  Save this article

Statistical inference links data and theory in network science

Author

Listed:
  • Leto Peel

    (Maastricht University)

  • Tiago P. Peixoto

    (Central European University)

  • Manlio De Domenico

    (University of Padua)

Abstract

The number of network science applications across many different fields has been rapidly increasing. Surprisingly, the development of theory and domain-specific applications often occur in isolation, risking an effective disconnect between theoretical and methodological advances and the way network science is employed in practice. Here we address this risk constructively, discussing good practices to guarantee more successful applications and reproducible results. We endorse designing statistically grounded methodologies to address challenges in network science. This approach allows one to explain observational data in terms of generative models, naturally deal with intrinsic uncertainties, and strengthen the link between theory and applications.

Suggested Citation

  • Leto Peel & Tiago P. Peixoto & Manlio De Domenico, 2022. "Statistical inference links data and theory in network science," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-34267-9
    DOI: 10.1038/s41467-022-34267-9
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-022-34267-9
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-022-34267-9?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. Riccardo Gallotti & Francesco Valle & Nicola Castaldo & Pierluigi Sacco & Manlio De Domenico, 2020. "Assessing the risks of ‘infodemics’ in response to COVID-19 epidemics," Nature Human Behaviour, Nature, vol. 4(12), pages 1285-1293, December.
    2. Zhesi Shen & Wen-Xu Wang & Ying Fan & Zengru Di & Ying-Cheng Lai, 2014. "Reconstructing propagation networks with natural diversity and identifying hidden sources," Nature Communications, Nature, vol. 5(1), pages 1-10, September.
    3. Michael T. SCHAUB & Jean-Charles DELVENNE, 2017. "The many facets of community detection in complex networks," LIDAM Reprints CORE 2890, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    4. Weiran Cai & Jordan Snyder & Alan Hastings & Raissa M. D’Souza, 2020. "Mutualistic networks emerging from adaptive niche-based interactions," Nature Communications, Nature, vol. 11(1), pages 1-10, December.
    5. John D. Medaglia & Perry Zurn & Walter Sinnott-Armstrong & Danielle S. Bassett, 2017. "Mind control as a guide for the mind," Nature Human Behaviour, Nature, vol. 1(6), pages 1-8, June.
    6. Jose Casadiego & Mor Nitzan & Sarah Hallerberg & Marc Timme, 2017. "Model-free inference of direct network interactions from nonlinear collective dynamics," Nature Communications, Nature, vol. 8(1), pages 1-10, December.
    7. 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.
    8. V. Rosato & L. Issacharoff & F. Tiriticco & S. Meloni & S. De Porcellinis & R. Setola, 2008. "Modelling interdependent infrastructures using interacting dynamical models," International Journal of Critical Infrastructures, Inderscience Enterprises Ltd, vol. 4(1/2), pages 63-79.
    9. Jakob Runge & Vladimir Petoukhov & Jonathan F. Donges & Jaroslav Hlinka & Nikola Jajcay & Martin Vejmelka & David Hartman & Norbert Marwan & Milan Paluš & Jürgen Kurths, 2015. "Identifying causal gateways and mediators in complex spatio-temporal systems," Nature Communications, Nature, vol. 6(1), pages 1-10, December.
    10. 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.
    11. Niklas Boers & Bedartha Goswami & Aljoscha Rheinwalt & Bodo Bookhagen & Brian Hoskins & Jürgen Kurths, 2019. "Complex networks reveal global pattern of extreme-rainfall teleconnections," Nature, Nature, vol. 566(7744), pages 373-377, February.
    12. Jean-Gabriel Young & Fernanda S. Valdovinos & M. E. J. Newman, 2021. "Reconstruction of plant–pollinator networks from observational data," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
    13. 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.
    14. Duncan J. Watts & Steven H. Strogatz, 1998. "Collective dynamics of ‘small-world’ networks," Nature, Nature, vol. 393(6684), pages 440-442, June.
    15. Roger Guimerà & Luís A. Nunes Amaral, 2005. "Functional cartography of complex metabolic networks," Nature, Nature, vol. 433(7028), pages 895-900, February.
    16. Michele Tumminello & Salvatore Miccichè & Fabrizio Lillo & Jyrki Piilo & Rosario N Mantegna, 2011. "Statistically Validated Networks in Bipartite Complex Systems," PLOS ONE, Public Library of Science, vol. 6(3), pages 1-11, March.
    17. G. Cencetti & G. Santin & A. Longa & E. Pigani & A. Barrat & C. Cattuto & S. Lehmann & M. Salathé & B. Lepri, 2021. "Digital proximity tracing on empirical contact networks for pandemic control," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
    18. Sergey V. Buldyrev & Roni Parshani & Gerald Paul & H. Eugene Stanley & Shlomo Havlin, 2010. "Catastrophic cascade of failures in interdependent networks," Nature, Nature, vol. 464(7291), pages 1025-1028, April.
    19. Nowicki K. & Snijders T. A. B., 2001. "Estimation and Prediction for Stochastic Blockstructures," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1077-1087, September.
    20. José M. Montoya & Stuart L. Pimm & Ricard V. Solé, 2006. "Ecological networks and their fragility," Nature, Nature, vol. 442(7100), pages 259-264, July.
    21. Baruch Barzel & Yang-Yu Liu & Albert-László Barabási, 2015. "Constructing minimal models for complex system dynamics," Nature Communications, Nature, vol. 6(1), pages 1-8, November.
    22. Ugo Bastolla & Miguel A. Fortuna & Alberto Pascual-García & Antonio Ferrera & Bartolo Luque & Jordi Bascompte, 2009. "The architecture of mutualistic networks minimizes competition and increases biodiversity," Nature, Nature, vol. 458(7241), pages 1018-1020, April.
    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. Arthur, Rudy, 2023. "Discovering block structure in networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 613(C).
    2. Hadi Vafaii & Francesca Mandino & Gabriel Desrosiers-Grégoire & David O’Connor & Marija Markicevic & Xilin Shen & Xinxin Ge & Peter Herman & Fahmeed Hyder & Xenophon Papademetris & Mallar Chakravarty , 2024. "Multimodal measures of spontaneous brain activity reveal both common and divergent patterns of cortical functional organization," Nature Communications, Nature, vol. 15(1), pages 1-19, December.

    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 & Dong, Qiang & Zhou, Tao, 2021. "Link prediction via controlling the leading eigenvector," Applied Mathematics and Computation, Elsevier, vol. 411(C).
    2. Huan Wang & Chuang Ma & Han-Shuang Chen & Ying-Cheng Lai & Hai-Feng Zhang, 2022. "Full reconstruction of simplicial complexes from binary contagion and Ising data," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    3. Aslan, Serpil & Kaya, Buket & Kaya, Mehmet, 2019. "Predicting potential links by using strengthened projections in evolving bipartite networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 998-1011.
    4. Lee, Yan-Li & Zhou, Tao, 2021. "Collaborative filtering approach to link prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 578(C).
    5. Peng Liu & Liang Gui & Huirong Wang & Muhammad Riaz, 2022. "A Two-Stage Deep-Learning Model for Link Prediction Based on Network Structure and Node Attributes," Sustainability, MDPI, vol. 14(23), pages 1-15, December.
    6. Liu, Zhenfeng & Feng, Jian & Uden, Lorna, 2023. "Technology opportunity analysis using hierarchical semantic networks and dual link prediction," Technovation, Elsevier, vol. 128(C).
    7. 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).
    8. Guan-Nan Wang & Hui Gao & Lian Chen & Dennis N A Mensah & Yan Fu, 2015. "Predicting Positive and Negative Relationships in Large Social Networks," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-14, June.
    9. Bhattacharjee, Biplab & Kumar, Rajiv & Senthilkumar, Arunachalam, 2022. "Unidirectional and bidirectional LSTM models for edge weight predictions in dynamic cross-market equity networks," International Review of Financial Analysis, Elsevier, vol. 84(C).
    10. 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.
    11. 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.
    12. 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).
    13. 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).
    14. 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.
    15. 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).
    16. 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).
    17. Zhou, Tao, 2023. "Discriminating abilities of threshold-free evaluation metrics in link prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 615(C).
    18. Zhang, Xuejun & Pang, Wenbo & Xia, Yongxiang, 2018. "An intermediary probability model for link prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 902-912.
    19. 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.
    20. Ding, Rui & Ujang, Norsidah & Hamid, Hussain bin & Manan, Mohd Shahrudin Abd & He, Yuou & Li, Rong & Wu, Jianjun, 2018. "Detecting the urban traffic network structure dynamics through the growth and analysis of multi-layer networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 800-817.

    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:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-34267-9. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    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.