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Spectral partitioning: the more eigenvectors, the better

Authors:

Charles J. Alpert,

So-Zen YaoAuthors Info & Claims

DAC '95: Proceedings of the 32nd annual ACM/IEEE Design Automation Conference

Pages 195 - 200

https://doi.org/10.1145/217474.217529

Published: 01 January 1995 Publication History

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References

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Cited By

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Mondal RIgnatova EWalke DBroneske DSaake GHeyer R(2024)Clustering graph data: the roadmap to spectral techniquesDiscover Artificial Intelligence10.1007/s44163-024-00102-x4:1Online publication date: 22-Jan-2024
https://doi.org/10.1007/s44163-024-00102-x
Çatalyürek ÜDevine KFaraj MGottesbüren LHeuer TMeyerhenke HSanders PSchlag SSchulz CSeemaier DWagner D(2023)More Recent Advances in (Hyper)Graph PartitioningACM Computing Surveys10.1145/357180855:12(1-38)Online publication date: 2-Mar-2023
https://dl.acm.org/doi/10.1145/3571808
Bolla MWinstein VYou TSeidl FAbdelkhalek F(2022)Regularity-based spectral clustering and mapping the Fiedler-carpetSpecial Matrices10.1515/spma-2022-016710:1(394-416)Online publication date: 10-Jun-2022
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Index Terms

Spectral partitioning: the more eigenvectors, the better
1. Mathematics of computing
  1. Discrete mathematics
    1. Graph theory

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MULTI-LEVEL SPECTRAL HYPERGRAPH PARTITIONING WITH ARBITRARY VERTEX SIZES

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DAC '95: Proceedings of the 32nd annual ACM/IEEE Design Automation Conference

January 1995

760 pages

ISBN:0897917251

DOI:10.1145/217474

Chairman:
Bryan T. Preas
Xerox Parc, Palo Alto, CA

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 01 January 1995

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June 12 - 16, 1995

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Mondal RIgnatova EWalke DBroneske DSaake GHeyer R(2024)Clustering graph data: the roadmap to spectral techniquesDiscover Artificial Intelligence10.1007/s44163-024-00102-x4:1Online publication date: 22-Jan-2024
https://doi.org/10.1007/s44163-024-00102-x
Çatalyürek ÜDevine KFaraj MGottesbüren LHeuer TMeyerhenke HSanders PSchlag SSchulz CSeemaier DWagner D(2023)More Recent Advances in (Hyper)Graph PartitioningACM Computing Surveys10.1145/357180855:12(1-38)Online publication date: 2-Mar-2023
https://dl.acm.org/doi/10.1145/3571808
Bolla MWinstein VYou TSeidl FAbdelkhalek F(2022)Regularity-based spectral clustering and mapping the Fiedler-carpetSpecial Matrices10.1515/spma-2022-016710:1(394-416)Online publication date: 10-Jun-2022
https://doi.org/10.1515/spma-2022-0167
Wang MHan HHuang ZXie J(2022)Unsupervised spectral feature selection algorithms for high dimensional dataFrontiers of Computer Science10.1007/s11704-022-2135-017:5Online publication date: 24-Dec-2022
https://doi.org/10.1007/s11704-022-2135-0
Yang QLi ZHan GGao WZhu SWu XDeng Y(2022)An improvement of spectral clustering algorithm based on fast diffusion search for natural neighbor and affinity propagationThe Journal of Supercomputing10.1007/s11227-022-04456-w78:12(14597-14625)Online publication date: 1-Aug-2022
https://dl.acm.org/doi/10.1007/s11227-022-04456-w
Löffler MZhang AZhou H(2021)Optimality of spectral clustering in the Gaussian mixture modelThe Annals of Statistics10.1214/20-AOS204449:5Online publication date: 1-Oct-2021
https://doi.org/10.1214/20-AOS2044
Acer SBoman EGlusa CRajamanickam S(2021)Sphynx: A parallel multi-GPU graph partitioner for distributed-memory systemsParallel Computing10.1016/j.parco.2021.102769106(102769)Online publication date: Sep-2021
https://doi.org/10.1016/j.parco.2021.102769
Lahav ATal A(2020)MeshWalkerACM Transactions on Graphics10.1145/3414685.341780639:6(1-13)Online publication date: 27-Nov-2020
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Li XKao BShan CYin DEster MGupta RLiu YShah MRajan STang JPrakash B(2020)CAST: A Correlation-based Adaptive Spectral Clustering Algorithm on Multi-scale DataProceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3394486.3403086(439-449)Online publication date: 23-Aug-2020
https://dl.acm.org/doi/10.1145/3394486.3403086
Acer SBoman ERajamanickam S(2020)SPHYNX: Spectral Partitioning for HYbrid aNd aXelerator-enabled systems2020 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)10.1109/IPDPSW50202.2020.00082(440-449)Online publication date: May-2020
https://doi.org/10.1109/IPDPSW50202.2020.00082
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New spectral graph partitioning algorithms

Multilevel spectral hypergraph partitioning with arbitrary vertex sizes

MULTI-LEVEL SPECTRAL HYPERGRAPH PARTITIONING WITH ARBITRARY VERTEX SIZES

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New spectral graph partitioning algorithms

Multilevel spectral hypergraph partitioning with arbitrary vertex sizes

MULTI-LEVEL SPECTRAL HYPERGRAPH PARTITIONING WITH ARBITRARY VERTEX SIZES

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