Summary of Towards Faster Graph Partitioning Via Pre-training and Inductive Inference, by Meng Qin et al.
Towards Faster Graph Partitioning via Pre-training and Inductive Inference
by Meng Qin, Chaorui Zhang, Yu Gao, Yibin Ding, Weipeng Jiang, Weixi Zhang, Wei Han, Bo Bai
First submitted to arxiv on: 1 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 proposed PR-GPT method is a novel approach to graph partitioning (GP) that leverages pre-training and refinement techniques. By first training a deep graph learning (DGL) model on small synthetic graphs, the authors demonstrate how they can directly generalize this model to large graphs and derive feasible GP results. The refined partition is then used as an initialization for an efficient GP method like InfoMap, allowing for both high-quality and efficient partitioning. This approach also enables streaming GP by reducing the scale of the graph to be processed. Experimental results on the Graph Challenge benchmark show that PR-GPT can achieve faster GP on large-scale graphs without significant quality degradation compared to running a refinement method from scratch. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary PR-GPT is a new way to divide big networks into smaller groups. It uses two steps: first, it learns from small fake networks, and then it applies what it learned to bigger real networks. This helps make the process faster and better. The technique also lets us do something called “streaming” which means we can keep working on a network even if it’s really big. When tested on a special benchmark, PR-GPT showed that it could be just as good as other methods but much faster. |
Keywords
» Artificial intelligence » Gpt
