Pursuing more effective graph spectral sparsifiers via approximate trace reduction-Reference-Cited by-同舟云学术

Pursuing more effective graph spectral sparsifiers via approximate trace reduction

Published:2022-07-10 Issue: Volume: Page:
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Container-title:Proceedings of the 59th ACM/IEEE Design Automation Conference
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Author:

Liu Zhiqiang¹,Yu Wenjian¹

Affiliation:

1. Tsinghua Univ., Beijing, China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3489517.3530511

Reference21 articles.

1. A. Aghdaei , Z. Zhao , and Z. Feng . 2021. HyperSF: spectral hypergraph coarsening via flow-based local clustering . In 2021 IEEE/ACM International Conference on Computer-Aided Design (ICCAD). A. Aghdaei, Z. Zhao, and Z. Feng. 2021. HyperSF: spectral hypergraph coarsening via flow-based local clustering. In 2021 IEEE/ACM International Conference on Computer-Aided Design (ICCAD).

2. J. Batson , D. Spielman , and N. Srivastava . 2009. Twice-Ramanujan sparsifiers . In Proc. ACM STOC. 255--262 . J. Batson, D. Spielman, and N. Srivastava. 2009. Twice-Ramanujan sparsifiers. In Proc. ACM STOC. 255--262.

3. Algorithm 887

4. The university of Florida sparse matrix collection

5. Spectral graph sparsification in nearly-linear time leveraging efficient spectral perturbation analysis

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