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arXiv · 2403.00551

Inferences for Random Graphs Evolved by Clustering Attachment

Natalia Markovich·Maksim Ryzhov·Marijus Vaičiulis
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Abstract

The evolution of random undirected graphs by the clustering attachment (CA) both without node and edge deletion and with uniform node or edge deletion is investigated. Theoretical results are obtained for the CA without node and edge deletion when a newly appended node is connected to two existing nodes of the graph at each evolution step. Theoretical results concern to (1) the sequence of increments of the consecutive mean clustering coefficients tends to zero; (2) the sequences of node degrees and triangle counts of any fixed node which are proved to be submartingales. These results were obtained for any initial graph. The simulation study is provided for the CA with uniform node or edge deletion and without any deletion. It is shown that (1) the CA leads to light-tailed distributed node degrees and triangle counts; (2) the average clustering coefficient tends to a constant over time; (3) the mean node degree and the mean triangle count increase over time with the rate depending on the parameters of the CA. The exposition is accompanied by a real data study.

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Natalia Markovich, Maksim Ryzhov, Marijus Vaičiulis. 2024-03-01. Inferences for Random Graphs Evolved by Clustering Attachment. https://arxiv.org/abs/2403.00551

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