Hostname: page-component-78c5997874-m6dg7 Total loading time: 0 Render date: 2024-11-10T16:49:32.722Z Has data issue: false hasContentIssue false

Introduction to the special issue on COMPLEX NETWORKS 2019

Published online by Cambridge University Press:  05 August 2021

Hocine Cherifi*
Affiliation:
LIB, University of Burgundy, France
Luis M. Rocha
Affiliation:
Indiana University (e-mail: rocha@indiana.edu)
*
*Corresponding author. Email: hocine.cherifi@gmail.com

Abstract

Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
Type
Introduction
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, 123.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, 119.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, 138.CrossRefGoogle Scholar
Martin, C., & Niemeyer, P. (2020). On the impact of network size and average degree on the robustness of centrality measures. Network Science, 122.CrossRefGoogle Scholar
Molter, H., Niedermeier, R., & Renken, M. (2020). Isolation concepts applied to temporal clique enumeration. Network Science, 123.CrossRefGoogle Scholar
Orman, K., Labatut, V., & Cherifi, H. (2013). An empirical study of the relation between community structure and transitivity. In Complex networks (pp. 99110). Berlin, Heidelberg: Springer.CrossRefGoogle Scholar
Saucan, E., Samal, A., & Jost, J. (2020). A simple differential geometry for complex networks. Network Science, 128.CrossRefGoogle Scholar
Wilkerson, G. J., & Moschoyiannis, S. (2021). Logic and learning in network cascades. Network Science, 118.CrossRefGoogle Scholar