Towards Ideal Temporal Graph Neural Networks: Evaluations and Conclusions after 10,000 GPU Hours

Summary: Directed 10k+ GPU-hour design-space search using a unified, optimized TGNN codebase to remove implementation/benchmarking confounds and fairly compare modules. Finds modern neighbor sampling + attention outperform uniform/MLP‑Mixer; static node memory competitive and memory choice should follow dataset repetition patterns. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID: 14251
Venue: VLDB
Year: 2025
Pagerank: 4.1945683e-05
Overall Rank: 10,887 | 24.27%
DOI: 10.14778/3717755.3717758

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Rank	Citing Paper	Year	Venue	Pagerank

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Showing 6 of 6 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
636	APAN: Asynchronous Propagation Attention Network for Real-time Temporal Graph Embedding	2021	SIGMOD	0.00018846494
1,387	TGL: A General Framework for Temporal GNN Training on Billion-Scale Graphs	2022	VLDB	0.00012261568
3,709	Zebra: When Temporal Graph Neural Networks Meet Temporal Personalized PageRank	2023	VLDB	6.8242482e-05
4,047	Orca: Scalable Temporal Graph Neural Network Training with Theoretical Guarantees	2023	SIGMOD	6.4972105e-05
5,007	Algorithm and System Co-design for Efficient Subgraph-based Graph Representation Learning	2022	VLDB	5.763689e-05
5,475	ETC: Efficient Training of Temporal Graph Neural Networks over Large-scale Dynamic Graphs	2024	VLDB	5.4869706e-05

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