Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Metrics for Graph Comparison: A Practitioner’s Guide

Peter Wills, View ORCID ProfileFrançois G. Meyer
doi: https://doi.org/10.1101/611509
Peter Wills
1Applied Mathematics, University of Colorado at Boulder, Boulder CO 80305
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
François G. Meyer
1Applied Mathematics, University of Colorado at Boulder, Boulder CO 80305
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for François G. Meyer
  • For correspondence: fmeyer@colorado.edu
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

Abstract

Comparison of graph structure is a ubiquitous task in data analysis and machine learning, with diverse applications in fields such as neuroscience [1], cyber security [2], social network analysis [3], and bioinformatics [4], among others. Discovery and comparison of structures such as modular communities, rich clubs, hubs, and trees in data in these fields yields 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 (also known as λ distances) and distances based on node affinities (such as DeltaCon [5]). However, there has of yet been no comparative study of the efficacy of these distance measures in discerning between common graph topologies and 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 empirical datasets. We put forward a multi-scale picture of graph structure, in which the effect of global and local structure upon the distance measures is considered. We make recommendations on the applicability of different distance measures to empirical graph data problem based on this multi-scale view. Finally, we introduce the Python library NetComp which implements the graph distances used in this work.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
Back to top
PreviousNext
Posted April 18, 2019.
Download PDF
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Metrics for Graph Comparison: A Practitioner’s Guide
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Metrics for Graph Comparison: A Practitioner’s Guide
Peter Wills, François G. Meyer
bioRxiv 611509; doi: https://doi.org/10.1101/611509
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
Metrics for Graph Comparison: A Practitioner’s Guide
Peter Wills, François G. Meyer
bioRxiv 611509; doi: https://doi.org/10.1101/611509

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Neuroscience
Subject Areas
All Articles
  • Animal Behavior and Cognition (3602)
  • Biochemistry (7567)
  • Bioengineering (5522)
  • Bioinformatics (20782)
  • Biophysics (10325)
  • Cancer Biology (7978)
  • Cell Biology (11635)
  • Clinical Trials (138)
  • Developmental Biology (6602)
  • Ecology (10200)
  • Epidemiology (2065)
  • Evolutionary Biology (13611)
  • Genetics (9539)
  • Genomics (12844)
  • Immunology (7919)
  • Microbiology (19538)
  • Molecular Biology (7657)
  • Neuroscience (42081)
  • Paleontology (308)
  • Pathology (1257)
  • Pharmacology and Toxicology (2201)
  • Physiology (3267)
  • Plant Biology (7038)
  • Scientific Communication and Education (1294)
  • Synthetic Biology (1951)
  • Systems Biology (5426)
  • Zoology (1116)