Graph Signal Processing for Directed Graphs Based on the Hermitian Laplacian-Reference-Cited by-同舟云学术

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Graph Signal Processing for Directed Graphs Based on the Hermitian Laplacian

Published:2020 Issue: Volume: Page:447-463
ISSN:0302-9743
Container-title:Machine Learning and Knowledge Discovery in Databases
language:
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Author:

Furutani Satoshi,Shibahara Toshiki,Akiyama Mitsuaki,Hato Kunio,Aida Masaki

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-46150-8_27

Reference35 articles.

1. Anis, A., El Gamal, A., Avestimehr, S., Ortega, A.: Asymptotic justification of bandlimited interpolation of graph signals for semi-supervised learning. In: 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5461–5465. IEEE (2015)

2. Berkolaiko, G.: Nodal count of graph eigenfunctions via magnetic perturbation. Anal. PDE 6(5), 1213–1233 (2013)

3. Bronstein, M.M., Bruna, J., LeCun, Y., Szlam, A., Vandergheynst, P.: Geometric deep learning: going beyond Euclidean data. IEEE Signal Process. Mag. 34(4), 18–42 (2017)

4. Chen, S., Varma, R., Sandryhaila, A., Kovačević, J.: Discrete signal processing on graphs: sampling theory. IEEE Trans. Signal Process. 63(24), 6510–6523 (2015)

5. Chung, F.: Laplacians and the cheeger inequality for directed graphs. Ann. Comb. 9(1), 1–19 (2005). https://doi.org/10.1007/s00026-005-0237-z

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