Corrigendum: A Comprehensive Benchmark on Spectral GNNs: The Impact on Efficiency, Memory, and Effectiveness: [Experiments & Analysis]

This is a corrigendum for the article ''A Comprehensive Benchmark on Spectral GNNs: The Impact on Efficiency, Memory, and Effectiveness: [Experiments & Analysis]'' published in Proc. ACM Manag. Data 3, 4 (SIGMOD), Article 238 (September 2025), 29 pages.

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Publication Details

Journal
Proceedings of the ACM on Management of Data
Published
2026-04-02
DOI
https://doi.org/10.1145/3803526
Primary Topic
Advanced Graph Neural Networks
Type
article
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Corrigendum: A Comprehensive Benchmark on Spectral GNNs: The Impact on Efficiency, Memory, and Effectiveness: [Experiments & Analysis]

Zulun Zhu, Laks V. S. Lakshmanan, Siqiang Luo, Ningyi Liao
Proceedings of the ACM on Management of Data
Advanced Graph Neural Networks
article

Corrigendum: A Comprehensive Benchmark on Spectral GNNs: The Impact on Efficiency, Memory, and Effectiveness: [Experiments & Analysis]

Zulun Zhu, Laks V. S. Lakshmanan, Siqiang Luo, Ningyi Liao
article en

Abstract

This is a corrigendum for the article ''A Comprehensive Benchmark on Spectral GNNs: The Impact on Efficiency, Memory, and Effectiveness: [Experiments & Analysis]'' published in Proc. ACM Manag. Data 3, 4 (SIGMOD), Article 238 (September 2025), 29 pages.

Proceedings of the ACM on Management of DataVol. 4(1)
University of British Columbia (CA), Nanyang Technological University (SG)
Openalex Percentile: Top 28%
Advanced Graph Neural Networks
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