Published February 14, 2022
| Version v2
Software
Open
BNS-GCN: Efficient Full-Graph Training of Graph Convolutional Networks with Partition-Parallelism and Random Boundary Node Sampling
Authors/Creators
- 1. Rice University
- 2. University of Illinois at Urbana-Champaign
- 3. Pacific Northwest National Laboratory
Description
Artifacts required for the evaluation of our MLSys 2022 paper.
Files
BNS-GCN.zip
Files
(37.1 kB)
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