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

Bibliographic Data

ID6161633
AuthorsMarcell Nagy (0000-0001-5666-7777, Budapest University of Technology and Economics, corresponding author), Roland Molontay (0000-0002-0666-5279, Budapest University of Technology and Economics)
Year2022
Volume10
Issue2
Pages146-169
Publication date2022-05-20
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueNetwork Science (JOURNAL)
Journal identifiersISSN: 2050-1250 • E-ISSN: 2050-1242
PublisherCambridge University Press (PUBLISHER • US)
DOI10.1017/nws.2022.14
OpenAlexW4280634094
LanguageEN
Citations received1
References cited58

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

Cluster analysis · Clustering coefficient · Complex network · Data mining · Graph · Machine learning · Model selection · Network analysis · Network model · Similarity (geometry · Bioinformatics and Genomic Networks · Complex Network Analysis Techniques · Computer Science · Mental Health Research Topics · Artificial Intelligence · Theoretical Computer Science

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