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Introduction to the special issue on COMPLEX NETWORKS 2019
Published online by Cambridge University Press: 05 August 2021
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- © The Author(s), 2021. Published by Cambridge University Press
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