Random Graph Models and Their Application to Twitter Network Analysis

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/).

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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  -