Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
April 10, 2026Proceedings of the ACM on Management of Data

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

View Full Paper
Ask AI
Bookmark
Share

Authors

NLNingyi LiaoHLHuan LiuZZZulun Zhu

Discussion

Loading...

Member takes

Overview

This corrigendum addresses corrections related to spectral GNN benchmarks, highlighting implications for efficiency and effectiveness.

Key Points

  • This corrigendum aims to clarify and correct details in the original benchmarking article on spectral GNNs.
  • Correction of previously published data related to efficiency, memory, and effectiveness of spectral GNNs.
  • Review of benchmark analysis methodologies used in the original article.
  • Updated benchmarks reflect changes in reported efficiency and memory use for spectral GNNs.
  • Clarified definitions enhance understanding of effectiveness metrics in spectral GNN evaluations.

Cite This Study

Liao et al. (2026) studied this question.

synapsesocial.com/papers/69d894ce6c1944d70ce05cb4https://doi.org/10.1145/3803526
View Full Paper
Ask AI
Bookmark
Share

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1A Comprehensive Benchmark on Spectral GNNs: The Impact on Efficiency, Memory, and Effectiveness2025 · 4 citations
  2. 2Benchmarking Spectral Graph Neural Networks: A Comprehensive Study on Effectiveness and Efficiency2024
  3. 3NoisyGL: A Comprehensive Benchmark for Graph Neural Networks under Label Noise2024 · 1 citations
  4. 4Rethinking the Effectiveness of Graph Classification Datasets in Benchmarks for Assessing GNNs2024
  5. 5Spectral GNN via Two-dimensional (2-D) Graph Convolution2024