Hostname: page-component-cd9895bd7-lnqnp Total loading time: 0 Render date: 2024-12-24T12:18:46.692Z Has data issue: false hasContentIssue false
  • English
  • Français

Introduction to the special issue on COMPLEX NETWORKS 2019

Published online by Cambridge University Press:  05 August 2021

Hocine Cherifi  and
Luis M. Rocha
Hocine Cherifi*
Affiliation:
LIB, University of Burgundy, France
Luis M. Rocha
Affiliation:
Indiana University (e-mail: [email protected])
*
*Corresponding author. Email: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

An abstract is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
Type
Introduction
Information
Network Science , Volume 9 , Special Issue S1: Complex Networks 2019 , October 2021 , pp. S1 - S3
Copyright
© The Author(s), 2021. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Antunes, N., Guo, T., & Pipiras, V. (2021). Sampling methods and estimation of triangle count distributions in large networks. Network Science, 1–23.CrossRefGoogle Scholar
Cherifi, H., Gaito, S., Mendes, J. F., Moro, E., & Rocha, L. M. (2020a). Complex Networks and Their Applications VIII - Volume 1, Proceedings of the Eighth International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2019, Lisbon, Portugal, December 10–12, 2019. Studies in Computational Intelligence 881, Springer, ISBN 978-3-030-36686-5. https://doi.org/10.1007/978-3-030-36687-2.CrossRefGoogle Scholar
Cherifi, H., Gaito, S., Mendes, J. F., Moro, E., & Rocha, L. M. (2020b). Complex Networks and Their Applications VIII - Volume 2, Proceedings of the Eighth International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2019, Lisbon, Portugal, December 10–12, 2019. Studies in Computational Intelligence 882, Springer, ISBN 978-3-030-36682-7. https://doi.org/10.1007/978-3-030-36683-4.CrossRefGoogle Scholar
Horn, P., & Nelsen, L. M. (2020). Gradient and Harnack-type estimates for pagerank. Network Science, 1–19.CrossRefGoogle Scholar
Liu, X., Chen, Y.-Z. J., Lui, J. C. S., & Avrachenkov, K. (2020). Learning to count: A deep learning framework for graphlet count estimation. Network Science, 1–38.CrossRefGoogle Scholar
Martin, C., & Niemeyer, P. (2020). On the impact of network size and average degree on the robustness of centrality measures. Network Science, 1–22.CrossRefGoogle Scholar
Molter, H., Niedermeier, R., & Renken, M. (2020). Isolation concepts applied to temporal clique enumeration. Network Science, 1–23.CrossRefGoogle Scholar
Orman, K., Labatut, V., & Cherifi, H. (2013). An empirical study of the relation between community structure and transitivity. In Complex networks (pp. 99–110). Berlin, Heidelberg: Springer.CrossRefGoogle Scholar
Saucan, E., Samal, A., & Jost, J. (2020). A simple differential geometry for complex networks. Network Science, 1–28.CrossRefGoogle Scholar
Wilkerson, G. J., & Moschoyiannis, S. (2021). Logic and learning in network cascades. Network Science, 1–18.CrossRefGoogle Scholar