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ADGNN: Towards Scalable GNN Training with Aggregation-Difference Aware Sampling

Summary: ADGNN enables scalable full-batch GNN training with a hybrid sampling engine in distributed systems. It introduces Aggregation Difference (AD) to bound sampling impact, plus AD-Sampling with adaptive sampling and AD-importance sampling for remote nodes, with result reuse; achieving up to 9x efficiency and similar accuracy. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6731
Venue
SIGMOD
Year
2023
Pagerank
4.7089968e-05
Overall Rank
7,566 | 47.37%
DOI
10.1145/3626716

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