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• #COMPARISON FACTOR FOR SPECTRA LINE SOFTWARE#

This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: Data and software can be obtained here. Received: Accepted: JanuPublished: February 12, 2020Ĭopyright: © 2020 Wills, Meyer. Finally, we introduce the Python library NetComp that implements the graph distances used in this work.Ĭitation: Wills P, Meyer FG (2020) Metrics for graph comparison: A practitioner’s guide. We make recommendations on the applicability of different distance measures to the analysis of empirical graph data based on this multi-scale view. 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. 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. 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. Common choices include spectral distances and distances based on node affinities. Often, two graphs are compared via a pairwise distance measure, with a small distance indicating structural similarity and vice versa. 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. 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.