ArticleProceedings of the National Academy of Sciences of the United States of America2018
Multiscale mixing patterns in networks.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed.
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- Parkinson's disease: Individual placebo responses are predicted by baseline expression of a striato-limbic network.Research square · 2026Article
- Homophily within and across groups.Nature communications · 2025Article
- Generalizing an outbreak cluster detection method for two groups: an application to rabies.Royal Society open science · 2025Article
- Disruption of global brain network topology in amnestic MCI: evidence from multimodal DTI and fMRI.Frontiers in neuroscience · 2025Article
- Longitudinal network changes and phenoconversion risk in isolated REM sleep behavior disorder.Nature communications · 2024Article
- Polyclonal regeneration of mouse bone marrow endothelial cells after irradiative conditioning.Cell reports · 2024Article
- Networking the desert plant microbiome, bacterial and fungal symbionts structure and assortativity in co-occurrence networks.Environmental microbiome · 2024Article
- Reassessing the modularity of gene co-expression networks using the Stochastic Block Model.PLoS computational biology · 2024Article
- Article
- Longitudinal Network Changes and Phenoconversion Risk in Isolated REM Sleep Behavior Disorder.Research square · 2024Article
- Reassessing the modularity of gene co-expression networks using the Stochastic Block Model.bioRxiv : the preprint server for biology · 2024Article
- Using adjusted local assortativity with Molecular Pixelation unveils colocalization of membrane proteins with immunological significance.Frontiers in immunology · 2024Article
- Socioeconomic reorganization of communication and mobility networks in response to external shocks.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- On the inadequacy of nominal assortativity for assessing homophily in networks.Scientific reports · 2023Article
- Quantifying ideological polarization on a network using generalized Euclidean distance.Science advances · 2023Article
- Adaptive and pathological connectivity responses in Parkinson's disease brain networks.Cerebral cortex (New York, N.Y. : 1991) · 2023Article
- Article
- Mapping nonlocal relationships between metadata and network structure with metadata-dependent encoding of random walks.Science advances · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Assortative mixing in networks is the tendency for nodes with the same attributes, or metadata, to link to each other. It is a property often found in social networks, manifesting as a higher tendency of links occurring between people of the same age, race, or political belief. Quantifying the level of assortativity or disassortativity (the preference of linking to nodes with different attributes) can shed light on the organization of complex networks. It is common practice to measure the level of assortativity according to the assortativity coefficient, or modularity in the case of categorical metadata. This global value is the average level of assortativity across the network and may not be a representative statistic when mixing patterns are heterogeneous. For example, a social network spanning the globe may exhibit local differences in mixing patterns as a consequence of differences in cultural norms. Here, we introduce an approach to localize this global measure so that we can describe the assortativity, across multiple scales, at the node level. Consequently, we are able to capture and qualitatively evaluate the distribution of mixing patterns in the network. We find that, for many real-world networks, the distribution of assortativity is skewed, overdispersed, and multimodal. Our method provides a clearer lens through which we can more closely examine mixing patterns in networks.
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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.