A Comprehensive Benchmark on Spectral GNNs: The Impact on Efficiency, Memory, and Effectiveness

Summary: Systematic benchmark of spectral GNNs: categorizes 35 models into 27 spectral filters and implements them in a unified spectral-oriented framework that scales to million-node graphs. Cross-scale evaluations expose nontrivial efficiency/memory/effectiveness trade-offs and give practical deployment guidelines. (summarized by gpt-5-mini on Feb 11 2026)

Paper ID: he168dd28269a8e0a
Venue: SIGMOD
Year: 2026
Pagerank: 4.9605343e-05
Overall Rank: 10,597 | 28.97%
DOI: 10.1145/3749156
PDF: Download (CC BY 4.0)

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Authors

1. Ningyi Liao (Nanyang Technological University)
2. Haoyu Liu (Nanyang Technological University)
3. Zulun Zhu (Nanyang Technological University)
4. Siqiang Luo (Nanyang Technological University)
5. Laks V.S. Lakshmanan (University of British Columbia)

BibTeX Citation

@inproceedings{liao_sigmod26,
        title = {{A Comprehensive Benchmark on Spectral GNNs: The Impact on Efficiency, Memory, and Effectiveness}},
        author = {Liao, Ningyi and Liu, Haoyu and Zhu, Zulun and Luo, Siqiang and Lakshmanan, Laks V.S.},
        series = {{SIGMOD} '26},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3749156},
        url = {https://dl.acm.org/doi/10.1145/3749156},
        year = {2026}
}

Incoming Citations (Sorted by Pagerank)

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Rank	Citing Paper	Year	Venue	Pagerank

Outgoing Citations (Sorted by Pagerank)

Showing 17 of 17 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
203	AliGraph: A Comprehensive Graph Neural Network Platform	2019	VLDB	0.00025234479
235	The Ubiquity of Large Graphs and Surprising Challenges of Graph Processing	2018	VLDB	0.00023701614
1,021	AGL: A Scalable System for Industrial-purpose Graph Machine Learning	2020	VLDB	0.00012408574
1,780	ByteGNN: Efficient Graph Neural Network Training at Large Scale	2022	VLDB	9.6601841e-05
2,434	Accelerating Large Scale Real-Time GNN Inference using Channel Pruning	2021	VLDB	8.4608438e-05
2,525	HET: Scaling out Huge Embedding Model Training via Cache-enabled Distributed Framework	2022	VLDB	8.3302991e-05
2,859	Ginex: SSD-enabled Billion-scale Graph Neural Network Training on a Single Machine via Provably Optimal In-memory Caching	2022	VLDB	7.9141159e-05
4,642	Algorithm and System Co-design for Efficient Subgraph-based Graph Representation Learning	2022	VLDB	6.4737342e-05
4,875	Comprehensive Evaluation of GNN Training Systems: A Data Management Perspective	2024	VLDB	6.3576702e-05
4,884	NeutronOrch: Rethinking Sample-based GNN Training under CPU-GPU Heterogeneous Environments	2024	VLDB	6.3564568e-05
5,148	SCARA: Scalable Graph Neural Networks with Feature-Oriented Optimization	2022	VLDB	6.2387892e-05
5,359	FreshGNN: Reducing Memory Access via Stable Historical Embeddings for Graph Neural Network Training	2024	VLDB	6.1498425e-05
6,124	GENTI: GPU-powered Walk-based Subgraph Extraction for Scalable Representation Learning on Dynamic Graphs	2024	VLDB	5.8623233e-05
6,686	Efficient Training of Graph Neural Networks on Large Graphs	2024	VLDB	5.6931383e-05
7,530	ADGNN: Towards Scalable GNN Training with Aggregation-Difference Aware Sampling	2023	SIGMOD	5.4862282e-05
8,649	D3-GNN: Dynamic Distributed Dataflow for Streaming Graph Neural Networks	2024	VLDB	5.2809021e-05
10,296	BIRD: Efficient Approximation of Bidirectional Hidden Personalized PageRank	2024	VLDB	5.031399e-05

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