Metrics for graph comparison: A practitioner’s guide

Publishing date: 2020-03-03

Published on: PLOS ONE

summary: 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. 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, no comparative study of the efficacy of these distance measures in discerning between common graph topologies at different structural scales has been presented. In this work, Wills and Meyer 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.

authors: Peter Wills, François G. Meyer

link to paper: 10.1371/journal.pone.0228728

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