Heta: Distributed Training of Heterogeneous Graph Neural Networks
Summary: Distributed HGNN training is communication-bound due to per-type feature dims and featureless nodes. Heta: relation-first aggregation, schema-aware meta-partitioning, and type-aware GPU cache cut cross-machine communication and yield ~5.3× speedups on large HetGs. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Yuchen Zhong
- 2. Junwei Su
- 3. Chuan Wu
- 4. Minjie Wang
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 4 | Pregel: A System for Large-Scale Graph Processing | 2010 | SIGMOD | 0.0019005923 |
| 278 | AliGraph: A Comprehensive Graph Neural Network Platform | 2019 | VLDB | 0.00029230623 |
| 411 | PyTorch Distributed: Experiences on Accelerating Data Parallel Training | 2020 | VLDB | 0.00023906921 |
| 1,160 | Sancus: Staleness-Aware Communication-Avoiding Full-Graph Decentralized Training in Large-Scale Graph Neural Networks | 2022 | VLDB | 0.00013586221 |
| 2,400 | ByteGNN: Efficient Graph Neural Network Training at Large Scale | 2022 | VLDB | 8.8955105e-05 |
| 5,321 | FreshGNN: Reducing Memory Access via Stable Historical Embeddings for Graph Neural Network Training | 2024 | VLDB | 5.5710441e-05 |
| 5,737 | Comprehensive Evaluation of GNN Training Systems: A Data Management Perspective | 2024 | VLDB | 5.3480667e-05 |
| 6,980 | OUTRE: An OUT-of-core De-REdundancy GNN Training Framework for Massive Graphs within A Single Machine | 2024 | VLDB | 4.8744298e-05 |
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