Multiscale network analysis through tail-greedy bottom-up approximation, with applications in neuroscience

Xinyu Kang, Piotr Fryzlewicz, Catherine Chu, Mark Kramer and Eric D. Kolaczyk

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: We propose the TGUH (Tail-Greedy Unbalanced Haar) transform for networks, which results in an orthonormal, adaptive decomposition of the network adjacency matrix into Haar-wavelet like components. The `tail-greediness' of the algorithm - indicating multiple greedy steps are taken in a single pass through the data - enables both fast computation and consistent estimation of network signals. We focus on development of our multiscale network decomposition and a corresponding method for network signal denoising. Moreover, we establish consistency of our resulting denoising methodology, present numerical simulations illustrating compression, and illustrate through application to signals on diffusion tensor imaging (DTI) networks.

Keywords: Matrix decomposition; Wavelet transforms; Noise reduction; Sparse matrices; Electronic mail; Estimation (search for similar items in EconPapers)
JEL-codes: C1 (search for similar items in EconPapers)
Date: 2018-04-16
New Economics Papers: this item is included in nep-ore
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Published in 2017 51st Asilomar Conference on Signals, Systems, and Computers, 16, April, 2018, pp. 1549-1554. ISSN: 2576-2303

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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:90021

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