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...

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Vydané v:PloS one Ročník 15; číslo 2; s. e0228728
Hlavní autori: Wills, Peter, Meyer, François G.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States Public Library of Science 12.02.2020
Public Library of Science (PLoS)
Predmet:
Applied mathematics
Big Data
Bioinformatics
Biology and Life Sciences
Comparative analysis
Comparative literature
Comparative studies
Computational biology
Computer and Information Sciences
Computer Graphics
Computer programs
Computer security
Cybersecurity
Cyberterrorism
Data analysis
Datasets
Distance measurement
Ecology and Environmental Sciences
Empirical analysis
Engineering and Technology
Graph comparison
Graphs
Information management
Internet security
Learning algorithms
Machine learning
Medicine and Health Sciences
Models, Theoretical
Modular structures
Multiscale analysis
Nervous system
Network analysis
Neurosciences
Physical Sciences
Research and Analysis Methods
Security
Social networks
Social organization
Social Sciences
Topology
United States > US
ISSN:1932-6203, 1932-6203
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Popis
Shrnutí: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.
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Competing Interests: The authors have declared that no competing interests exist.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0228728