ArticleNetwork neuroscience (Cambridge, Mass.)2019
Guided graph spectral embedding: Application to the
Article in Network neuroscience (Cambridge, Mass.), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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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Who cites it
5 citing papers in PubMed.
- Graph embeddings for identifying symmetries in Connectomes.bioRxiv : the preprint server for biology · 2025Article
- Large-scale comparative analysis reveals top graph signal processing features for subject identification.bioRxiv : the preprint server for biology · 2025Article
- Voxel-Wise Brain Graphs From Diffusion MRI: Intrinsic Eigenspace Dimensionality and Application to Functional MRI.IEEE open journal of engineering in medicine and biology · 2025Article
- The synaptic organization in theProceedings of the National Academy of Sciences of the United States of America · 2023Article
- Graph neural fields: A framework for spatiotemporal dynamical models on the human connectome.PLoS computational biology · 2021Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Graph spectral analysis can yield meaningful embeddings of graphs by providing insight into distributed features not directly accessible in nodal domain. Recent efforts in graph signal processing have proposed new decompositions-for example, based on wavelets and Slepians-that can be applied to filter signals defined on the graph. In this work, we take inspiration from these constructions to define a new guided spectral embedding that combines maximizing energy concentration with minimizing modified embedded distance for a given importance weighting of the nodes. We show that these optimization goals are intrinsically opposite, leading to a well-defined and stable spectral decomposition. The importance weighting allows us to put the focus on particular nodes and tune the trade-off between global and local effects. Following the derivation of our new optimization criterion, we exemplify the methodology on the
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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.