ArticleProceedings of the National Academy of Sciences of the United States of America2018
Global spectral clustering in dynamic 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 13 papers.
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Who cites it
13 citing papers in PubMed.
- PARROT: Phase-Altering Regulatory Rewiring Over Time.bioRxiv : the preprint server for biology · 2026Article
- An embedding-based distance for temporal graphs.Nature communications · 2024Article
- The genetics of spatiotemporal variation in cortical thickness in youth.Communications biology · 2024Article
- Article
- MuDCoD: multi-subject community detection in personalized dynamic gene networks from single-cell RNA sequencing.Bioinformatics (Oxford, England) · 2023Article
- Bias-adjusted spectral clustering in multi-layer stochastic block models.Journal of the American Statistical Association · 2023Article
- EXTENDED STOCHASTIC BLOCK MODELS WITH APPLICATION TO CRIMINAL NETWORKS.The annals of applied statistics · 2022Article
- Ranking of communities in multiplex spatiotemporal models of brain dynamics.Applied network science · 2022Article
- TPSC: a module detection method based on topology potential and spectral clustering in weighted networks and its application in gene co-expression module discovery.BMC bioinformatics · 2021Article
- Covariance-based sample selection for heterogeneous data: Applications to gene expression and autism risk gene detection.Journal of the American Statistical Association · 2021Article
- APOE and TREM2 regulate amyloid-responsive microglia in Alzheimer's disease.Acta neuropathologica · 2020Article
- Resting-State Functional Network Organization Is Stable Across Adolescent Development for Typical and Psychosis Spectrum Youth.Schizophrenia bulletin · 2020Article
- A Tensor-Based Framework for rs-fMRI Classification and Functional Connectivity Construction.Frontiers in neuroinformatics · 2020Article
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Authors and funding
4 authors.
Funding
Abstract
Community detection is challenging when the network structure is estimated with uncertainty. Dynamic networks present additional challenges but also add information across time periods. We propose a global community detection method, persistent communities by eigenvector smoothing (PisCES), that combines information across a series of networks, longitudinally, to strengthen the inference for each period. Our method is derived from evolutionary spectral clustering and degree correction methods. Data-driven solutions to the problem of tuning parameter selection are provided. In simulations we find that PisCES performs better than competing methods designed for a low signal-to-noise ratio. Recently obtained gene expression data from rhesus monkey brains provide samples from finely partitioned brain regions over a broad time span including pre- and postnatal periods. Of interest is how gene communities develop over space and time; however, once the data are divided into homogeneous spatial and temporal periods, sample sizes are very small, making inference quite challenging. Applying PisCES to medial prefrontal cortex in monkey rhesus brains from near conception to adulthood reveals dense communities that persist, merge, and diverge over time and others that are loosely organized and short lived, illustrating how dynamic community detection can yield interesting insights into processes such as brain development.
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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.