Random Graph Models and Their Application to Twitter Network Analysis
Authors
Kirill Shaposnikov, Irina Sagaeva, Alexey Grigoriev, Alexey Faizliev, Andrey Vlasov
Corresponding Author
Kirill Shaposnikov
Available Online 12 December 2019.
- DOI
- 10.2991/ahcs.k.191206.016How to use a DOI?
- Keywords
- social network analysis, classification, degree distribution, social graph
- Abstract
In this paper, we conducted an experiment for comparison of the graphs generated by Erdős-Rényi, Barabási-Albert, Bollobás-Riordan, Buckley–Osthus, Chung-Lu models and a web graph constructed using real data. Twitter data have been employed to construct social network, and C++ has been used for network analysis as well as network visualization. It was shown that distribution of degrees and clustering coefficient for this network follows the power law. A machine learning approach is used for empirical evaluation of the Erdős-Rényi, Barabási-Albert, Bollobás-Riordan, Buckley–Osthus, Chung-Lu models in comparison to the Twitter graph.
- Copyright
- © 2019, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Kirill Shaposnikov AU - Irina Sagaeva AU - Alexey Grigoriev AU - Alexey Faizliev AU - Andrey Vlasov PY - 2019 DA - 2019/12/12 TI - Random Graph Models and Their Application to Twitter Network Analysis BT - Proceedings of the Fourth Workshop on Computer Modelling in Decision Making (CMDM 2019) PB - Atlantis Press SP - 89 EP - 93 SN - 2589-4900 UR - https://doi.org/10.2991/ahcs.k.191206.016 DO - 10.2991/ahcs.k.191206.016 ID - Shaposnikov2019 ER -