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Network classification-based structural analysis of real networks and their model-generated counterparts

Published online by Cambridge University Press:  20 May 2022

Marcell Nagy Open the ORCID record for Marcell Nagy [Opens in a new window]  and
Roland Molontay Open the ORCID record for Roland Molontay [Opens in a new window]
Marcell Nagy*
Affiliation:
Department of Stochastics, Institute of Mathematics, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary
Roland Molontay
Affiliation:
Department of Stochastics, Institute of Mathematics, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary MTA-BME Stochastics Research Group, Műegyetem rkp. 3., H-1111 Budapest, Hungary
*
Corresponding author: Marcell Nagy, email: marcessz@math.bme.hu, molontay@math.bme.hu
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Abstract

Data-driven analysis of complex networks has been in the focus of research for decades. An important area of research is to study how well real networks can be described with a small selection of metrics, furthermore how well network models can capture the relations between graph metrics observed in real networks. In this paper, we apply machine-learning techniques to investigate the aforementioned problems. We study 500 real-world networks along with 2000 synthetic networks generated by four frequently used network models with previously calibrated parameters to make the generated graphs as similar to the real networks as possible. This paper unifies several branches of data-driven complex network analysis, such as the study of graph metrics and their pair-wise relationships, network similarity estimation, model calibration, and graph classification. We find that the correlation profiles of the structural measures significantly differ across network domains and the domain can be efficiently determined using a small selection of graph metrics. The structural properties of the network models with fixed parameters are robust enough to perform parameter calibration. The goodness-of-fit of the network models highly depends on the network domain. By solving classification problems, we find that the models lack the capability of generating a graph with a high clustering coefficient and relatively large diameter simultaneously. On the other hand, models are able to capture exactly the degree-distribution-related metrics.

Keywords

complex networksnetwork modelsmodel calibrationmodel stabilitydomain predictionnetwork characteristicsnetwork classificationnetwork similarity
Type
Research Article
Information
Network Science , Volume 10 , Issue 2 , June 2022 , pp. 146 - 169
Copyright
© The Author(s), 2022. Published by Cambridge University Press

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Footnotes

Action Editor: Christoph Stadtfeld

A preliminary version of this paper was presented at the Ninth International Conference on Complex Networks and their Applications (COMPLEX NETWORKS 2020).

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