Abstract
The paper’s main focus is the analysis of degree-degree correlation in complex networks generated following two growth models based on the preferential attachment mechanism: the Barabási-Albert model and the triadic closure model. The average nearest neighbor degree (ANND) value of k-degree nodes, defined as their neighbors’ average degree, is employed to quantify the degree-degree assortativity in the networks. First, we derive the dynamics of the average degree of neighbors for every node. Then we find the distributions of the ANND-values at each iteration in both networks. Results show that both networks are uncorrelated for nodes with high degrees, while for small degrees both networks exhibit degree-degree disassortativity.
This work was supported by the Ministry of Science and Higher Education of the Russian Federation in the framework of the basic part of the scientific research state task, project FSRR-2020-0006.
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Mironov, S., Sidorov, S., Malinskii, I. (2021). Degree-Degree Correlation in Networks with Preferential Attachment Based Growth. In: Teixeira, A.S., Pacheco, D., Oliveira, M., Barbosa, H., Gonçalves, B., Menezes, R. (eds) Complex Networks XII. CompleNet-Live 2021. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-030-81854-8_5
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