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Plexus: Taming Billion-edge Graphs with 3D Parallel Full-graph GNN Training

arXiv — cs.LG•Thursday, October 30, 2025 at 4:00:00 AM
PositiveArtificial Intelligence
Plexus has made significant strides in the field of graph neural networks (GNNs) by introducing a method for training on billion-edge graphs using 3D parallel full-graph techniques. This advancement is crucial as it addresses the limitations of traditional GNN training methods that struggle with large-scale graphs, which often exceed GPU memory capacity. By improving the efficiency of GNN training, Plexus opens up new possibilities for analyzing complex data structures, making it easier for researchers and developers to leverage the power of GNNs in various applications.
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