Towards Faster Graph Partitioning via Pre-training and Inductive Inference
<|reference_start|>Towards Faster Graph Partitioning via Pre-training and Inductive Inference: Graph partitioning (GP) is a classic problem that divides the node set of a graph into densely-connected blocks. Following the IEEE HPEC Graph Challenge and recent advances in pre-training techniques (e.g., large-language models), we propose PR-GPT (Pre-trained & Refined Graph ParTitioning) based on a novel pre-training & refinement paradigm. We first conduct the offline pre-training of a deep graph learning (DGL) model on small synthetic graphs with various topology properties. By using the inductive inference of DGL, one can directly generalize the pre-trained model (with frozen model parameters) to large graphs and derive feasible GP results. We also use the derived partition as a good initialization of an efficient GP method (e.g., InfoMap) to further refine the quality of partitioning. In this setting, the online generalization and refinement of PR-GPT can not only benefit from the transfer ability regarding quality but also ensure high inference efficiency without re-training. Based on a mechanism of reducing the scale of a graph to be processed by the refinement method, PR-GPT also has the potential to support streaming GP. Experiments on the Graph Challenge benchmark demonstrate that PR-GPT can ensure faster GP on large-scale graphs without significant quality degradation, compared with running a refinement method from scratch. We will make our code public at https://github.com/KuroginQin/PRGPT.<|reference_end|>
@article{qin2024towards,
    title={Towards Faster Graph Partitioning via Pre-training and Inductive
  Inference},
    author={Meng Qin, Chaorui Zhang, Yu Gao, Yibin Ding, Weipeng Jiang, Weixi
  Zhang, Wei Han, Bo Bai},
    journal={arXiv preprint arXiv:2409.00670},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.00670},
    primaryClass={cs.LG cs.SI}
}