Accelerating Large Scale Real-Time GNN Inference using Channel Pruning

Summary: Channel pruning via LASSO identifies influential GNN channels per layer for large-scale real-time inference. Two inference regimes and a feature-reuse scheme cut compute/memory, achieving 3.27x GPU and 6.67x CPU speedups with minimal accuracy loss. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 12346
Venue: VLDB
Year: 2021
Pagerank: 9.359876e-05
Overall Rank: 2,177 | 84.86%
DOI: 10.14778/3461535.3461547

Incoming Non-self Citations Over Time

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Incoming Citations (Sorted by Pagerank)

Showing 10 of 10 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
1,160	Sancus: Staleness-Aware Communication-Avoiding Full-Graph Decentralized Training in Large-Scale Graph Neural Networks	2022	VLDB	0.00013586221
5,007	Algorithm and System Co-design for Efficient Subgraph-based Graph Representation Learning	2022	VLDB	5.763689e-05
9,596	Scalable Graph Convolutional Network Training on Distributed-Memory Systems	2023	VLDB	4.319218e-05
9,677	Apt-Serve: Adaptive Request Scheduling on Hybrid Cache for Scalable LLM Inference Serving	2025	SIGMOD	4.3047774e-05
9,764	View-based Explanations for Graph Neural Networks	2024	SIGMOD	4.2856106e-05
10,011	A Comprehensive Benchmark on Spectral GNNs: The Impact on Efficiency, Memory, and Effectiveness	2026	SIGMOD	4.1945683e-05
10,233	Efficient GNN Training on Giant Graphs with Collective Batching and Scheduling	2026	VLDB	4.1945683e-05
10,663	Inference-friendly Graph Compression for Graph Neural Networks	2025	VLDB	4.1945683e-05
10,792	Graph Compression for Interpretable Graph Neural Network Inference At Scale	2025	VLDB	4.1945683e-05
11,079	Complex-Path: Effective and Efficient Node Ranking with Paths in Billion-Scale Heterogeneous Graphs	2024	VLDB	4.1945683e-05

Outgoing Citations (Sorted by Pagerank)

Showing 0 of 0 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank

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