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• W2904291726 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 C. elegans structural connectome. The results of our analyses confirm known observations on the nematode's neural network in terms of functionality and importance of cells. Compared with Laplacian embedding, the guided approach, focused on a certain class of cells (sensory neurons, interneurons, or motoneurons), provides more biological insights, such as the distinction between somatic positions of cells, and their involvement in low- or high-order processing functions." @default.
• W2904291726 created "2018-12-22" @default.
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• W2904291726 date "2019-01-01" @default.
• W2904291726 modified "2023-10-02" @default.
• W2904291726 title "Guided graph spectral embedding: Application to the <i>C. elegans</i> connectome" @default.
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• W2904291726 doi "https://doi.org/10.1162/netn_a_00084" @default.
• W2904291726 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6663470" @default.
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• W2904291726 hasPublicationYear "2019" @default.
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