Summary of Graphscale: a Framework to Enable Machine Learning Over Billion-node Graphs, by Vipul Gupta et al.
GraphScale: A Framework to Enable Machine Learning over Billion-node Graphs
by Vipul Gupta, Xin Chen, Ruoyun Huang, Fanlong Meng, Jianjun Chen, Yujun Yan
First submitted to arxiv on: 22 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC); Social and Information Networks (cs.SI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel approach to improve the scalability of Graph Neural Networks (GNNs) in both supervised and unsupervised learning settings, particularly for large graphs with over 1 billion nodes. The current bottleneck lies in the mini-batch sampling phase in GNNs and random walk sampling phase in unsupervised methods, which require storing features or embeddings in memory and frequent inter-worker communication during distributed training, leading to high communication overhead and computational inefficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper aims to address the scalability issue in graph neural networks for large graphs by improving the mini-batch sampling phase and random walk sampling phase. This will enable more efficient training and reduce the need for storing features or embeddings in memory, making it easier to handle large datasets. |
Keywords
» Artificial intelligence » Supervised » Unsupervised
