mlsanityData to 7 Oct 2026

SUGAR: Efficient Subgraph-level Training via Resource-aware Graph Partitioning

arXiv 2202.00075 ↗First posted 31 Jan 2022cs.LG cs.AIcited by 6

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Abstract (arXiv)

Graph Neural Networks (GNNs) have demonstrated a great potential in a variety of graph-based applications, such as recommender systems, drug discovery, and object recognition. Nevertheless, resource-efficient GNN learning is a rarely explored topic despite its many benefits for edge computing and Internet of Things (IoT) applications. To improve this state of affairs, this work proposes efficient subgraph-level training via resource-aware graph partitioning (SUGAR). SUGAR first partitions the initial graph into a set of disjoint subgraphs and then performs local training at the subgraph-level. We provide a theoretical analysis and conduct extensive experiments on five graph benchmarks to verify its efficacy in practice. Our results show that SUGAR can achieve up to 33 times runtime speedup and 3.8 times memory reduction on large-scale graphs. We believe SUGAR opens a new research direction towards developing GNN methods that are resource-efficient, hence suitable for IoT deployment.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 23 of its 43 references that are in the map. First posted on arXiv 31 Jan 2022.

Builds on

23 of 43 references.

References · 23

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11 more
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Cited by year

Citing papers per year: 2022: 1; 2023: 1; 2024: 2; 2025: 2.

6 papers.

  1. 2022Distributed Graph Neural Network Training: A Surveycited by 32
  2. 2023Comprehensive Evaluation of GNN Training Systems: A Data Management Perspectivecited by 9
  3. 2025Nonparametric Teaching for Graph Property Learnerscited by 5
  4. 2024Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligencecited by 2
  5. 2024FIT-GNN: Faster Inference Time for GNNs that 'FIT' in Memory Using Coarseningcited by 1
  6. 2025GCoDE: Efficient Device-Edge Co-Inference for GNNs via Architecture-Mapping Co-Searchcited by 0