ArticlePloS one2020
Metrics for graph comparison: A practitioner's guide.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
35 citing papers in PubMed.
- Information theory for hypergraph similarity.Science advances · 2026Article
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- Gene-environment-brain topology reveals clinical subtypes of depression in UK Biobank.Scientific reports · 2025Article
- Combinative Protein Expression of Immediate Early Genes c-Fos, Arc, and Npas4 Along Aversive and Appetitive Experience-Related Neural Networks.Hippocampus · 2025Article
- Pairwise graph edit distance characterizes the impact of the construction method on pangenome graphs.Bioinformatics (Oxford, England) · 2025Article
- The Transdiagnostic Connectome Project: an open dataset for studying brain-behavior relationships in psychiatry.Scientific data · 2025Article
- Applying causality to environmental security in Iraq.Scientific reports · 2025Article
- Leveraging advances in machine learning for the robust classification and interpretation of networks.Royal Society open science · 2025Article
- Care coordination and patient safety outcome: a graph-based approach.npj health systems · 2025Article
- Computational network biology analysis revealed COVID-19 severity markers: Molecular interplay between HLA-II with CIITA.PloS one · 2025Article
- An embedding-based distance for temporal graphs.Nature communications · 2024Article
- Article
- Intrinsic dimension as a multi-scale summary statistics in network modeling.Scientific reports · 2024Article
- The Transdiagnostic Connectome Project: a richly phenotyped open dataset for advancing the study of brain-behavior relationships in psychiatry.medRxiv : the preprint server for health sciences · 2024Article
- Article
- Quantifying synergy and redundancy between networks.Cell reports. Physical science · 2024Article
- Sparse spectral graph analysis and its application to gastric cancer drug resistance-specific molecular interplays identification.PloS one · 2024Article
- Mapping the configurational landscape and aggregation phase behavior of the tau protein fragment PHF6.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Robust mapping of spatiotemporal trajectories and cell-cell interactions in healthy and diseased tissues.Nature communications · 2023Article
- Spectral dynamics of guided edge removals and identifying transient amplifiers for death-Birth updating.Journal of mathematical biology · 2023Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.