DiskGNN: Bridging I/O Efficiency and Model Accuracy for Out-of-Core GNN Training

Summary: Offline sampling decouples graph sampling from computation, enabling batch features and avoiding accuracy loss. 4-level store, batched packing, and pipelined training exploit CPU/GPU hierarchy to cut I/O and deliver ~8x speed with identical accuracy. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 7077
Venue: SIGMOD
Year: 2025
Pagerank: 4.8297805e-05
Overall Rank: 7,108 | 50.56%
DOI: 10.1145/3709738

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
9,804	Capsule*: An Out-of-Core Training Mechanism for Colossal GNNs	2025	SIGMOD	4.2805224e-05
10,082	Gem: Scalable Monotonic Graph Processing Beyond Billion-Scale on a Single Machine	2026	SIGMOD	4.1945683e-05
10,647	Can Graph Reordering Speed Up Graph Neural Network Training? An Experimental Study	2025	VLDB	4.1945683e-05

Outgoing Citations (Sorted by Pagerank)

Showing 4 of 4 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
1,676	Speedup Graph Processing by Graph Ordering	2016	SIGMOD	0.00010946423
3,276	Ginex: SSD-enabled Billion-scale Graph Neural Network Training on a Single Machine via Provably Optimal In-memory Caching	2022	VLDB	7.2879718e-05
5,561	Accelerating Sampling and Aggregation Operations in GNN Frameworks with GPU Initiated Direct Storage Accesses	2024	VLDB	5.4332062e-05
6,446	Play like a Vertex: A Stackelberg Game Approach for Streaming Graph Partitioning	2024	SIGMOD	5.0588808e-05

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