ArticleNature communications2017
Quantification of network structural dissimilarities.
Article in Nature communications, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers, 1 of them a synthesis that pooled it.
What it found
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The trial behind it
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
52 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Environmental and host- and parasite-associated drivers of the flea-mammal interaction network structure differ between biogeographic realms.Parasitology research · 2025Article
- Functional and structural clustering of social relationship layers among college students for link prediction with applications to perceived drinking networks.Scientific reports · 2025Article
- An egonet based approach to effective weighted network comparison.Scientific reports · 2025Article
- Network-informed analysis of a multivariate trait-space reveals optimal trait selection.Communications biology · 2025Article
- Understanding complex systems through differential causal networks.Scientific reports · 2024Article
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- Intrinsic dimension as a multi-scale summary statistics in network modeling.Scientific reports · 2024Article
- Quantifying synergy and redundancy between networks.Cell reports. Physical science · 2024Article
- Comparison of Graph Distance Measures for Movie Similarity Using a Multilayer Network Model.Entropy (Basel, Switzerland) · 2024Article
- Directed Network Comparison Using Motifs.Entropy (Basel, Switzerland) · 2024Article
- Temporal segmentation of EEG based on functional connectivity network structure.Scientific reports · 2023Article
- The airway microbiome mediates the interaction between environmental exposure and respiratory health in humans.Nature medicine · 2023Article
- Diffusion capacity of single and interconnected networks.Nature communications · 2023Article
- Models of similarity in complex networks.PeerJ. Computer science · 2023Article
- Significant subgraph mining for neural network inference with multiple comparisons correction.Network neuroscience (Cambridge, Mass.) · 2023Article
- Improving gene regulatory network inference and assessment: The importance of using network structure.Frontiers in genetics · 2023Article
- The time-evolving epileptic brain network: concepts, definitions, accomplishments, perspectives.Frontiers in network physiology · 2023Review
- Flexibility in the social structure of male chimpanzees (Royal Society open science · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Identifying and quantifying dissimilarities among graphs is a fundamental and challenging problem of practical importance in many fields of science. Current methods of network comparison are limited to extract only partial information or are computationally very demanding. Here we propose an efficient and precise measure for network comparison, which is based on quantifying differences among distance probability distributions extracted from the networks. Extensive experiments on synthetic and real-world networks show that this measure returns non-zero values only when the graphs are non-isomorphic. Most importantly, the measure proposed here can identify and quantify structural topological differences that have a practical impact on the information flow through the network, such as the presence or absence of critical links that connect or disconnect connected components.
Identifiers
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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.