Journal article
Maximum entropy approach to massive graph spectrum learning with applications
- Abstract:
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We propose an alternative maximum entropy approach to learning the spectra of massive graphs. In contrast to state-of-the-art Lanczos algorithm for spectral density estimation and applications thereof, our approach does not require kernel smoothing. As the choice of kernel function and associated bandwidth heavily affect the resulting output, our approach mitigates these issues. Furthermore, we prove that kernel smoothing biases the moments of the spectral density. Our approach can be seen as...
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- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Preview, Version of record, pdf, 1.3MB, Terms of use)
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- Publisher copy:
- 10.3390/a15060209
Authors
Bibliographic Details
- Publisher:
- MDPI
- Journal:
- Algorithms More from this journal
- Volume:
- 15
- Issue:
- 6
- Article number:
- 209
- Publication date:
- 2022-06-15
- Acceptance date:
- 2022-05-26
- DOI:
- EISSN:
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1999-4893
Item Description
- Language:
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English
- Keywords:
- Pubs id:
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1267027
- Local pid:
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pubs:1267027
- Deposit date:
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2022-09-13
Terms of use
- Copyright holder:
- Granziol et al.
- Copyright date:
- 2022
- Rights statement:
- Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).
- Licence:
- CC Attribution (CC BY)
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