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Metrics for graph comparison: A practitioner’s guide
Comparison of graph structure is a ubiquitous task in data analysis and machine learning, with diverse applications in fields such as neuroscience, cyber security, social network analysis, and bioinformatics, among others. Discovery and comparison of structures such as modular communities, rich club...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7015405/ https://www.ncbi.nlm.nih.gov/pubmed/32050004 http://dx.doi.org/10.1371/journal.pone.0228728 |
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author | Wills, Peter Meyer, François G. |
author_facet | Wills, Peter Meyer, François G. |
author_sort | Wills, Peter |
collection | PubMed |
description | Comparison of graph structure is a ubiquitous task in data analysis and machine learning, with diverse applications in fields such as neuroscience, cyber security, social network analysis, and bioinformatics, among others. Discovery and comparison of structures such as modular communities, rich clubs, hubs, and trees yield insight into the generative mechanisms and functional properties of the graph. Often, two graphs are compared via a pairwise distance measure, with a small distance indicating structural similarity and vice versa. Common choices include spectral distances and distances based on node affinities. However, there has of yet been no comparative study of the efficacy of these distance measures in discerning between common graph topologies at different structural scales. In this work, we compare commonly used graph metrics and distance measures, and demonstrate their ability to discern between common topological features found in both random graph models and real world networks. We put forward a multi-scale picture of graph structure wherein we study the effect of global and local structures on changes in distance measures. We make recommendations on the applicability of different distance measures to the analysis of empirical graph data based on this multi-scale view. Finally, we introduce the Python library NetComp that implements the graph distances used in this work. |
format | Online Article Text |
id | pubmed-7015405 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-70154052020-02-26 Metrics for graph comparison: A practitioner’s guide Wills, Peter Meyer, François G. PLoS One Research Article Comparison of graph structure is a ubiquitous task in data analysis and machine learning, with diverse applications in fields such as neuroscience, cyber security, social network analysis, and bioinformatics, among others. Discovery and comparison of structures such as modular communities, rich clubs, hubs, and trees yield insight into the generative mechanisms and functional properties of the graph. Often, two graphs are compared via a pairwise distance measure, with a small distance indicating structural similarity and vice versa. Common choices include spectral distances and distances based on node affinities. However, there has of yet been no comparative study of the efficacy of these distance measures in discerning between common graph topologies at different structural scales. In this work, we compare commonly used graph metrics and distance measures, and demonstrate their ability to discern between common topological features found in both random graph models and real world networks. We put forward a multi-scale picture of graph structure wherein we study the effect of global and local structures on changes in distance measures. We make recommendations on the applicability of different distance measures to the analysis of empirical graph data based on this multi-scale view. Finally, we introduce the Python library NetComp that implements the graph distances used in this work. Public Library of Science 2020-02-12 /pmc/articles/PMC7015405/ /pubmed/32050004 http://dx.doi.org/10.1371/journal.pone.0228728 Text en © 2020 Wills, Meyer http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Wills, Peter Meyer, François G. Metrics for graph comparison: A practitioner’s guide |
title | Metrics for graph comparison: A practitioner’s guide |
title_full | Metrics for graph comparison: A practitioner’s guide |
title_fullStr | Metrics for graph comparison: A practitioner’s guide |
title_full_unstemmed | Metrics for graph comparison: A practitioner’s guide |
title_short | Metrics for graph comparison: A practitioner’s guide |
title_sort | metrics for graph comparison: a practitioner’s guide |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7015405/ https://www.ncbi.nlm.nih.gov/pubmed/32050004 http://dx.doi.org/10.1371/journal.pone.0228728 |
work_keys_str_mv | AT willspeter metricsforgraphcomparisonapractitionersguide AT meyerfrancoisg metricsforgraphcomparisonapractitionersguide |