Towards Faster Graph Partitioning via Pre-training and Inductive Inference

TL;DR

PR-GPT introduces a pre-training and inductive inference approach for graph partitioning that enables faster processing of large graphs with maintained quality, leveraging transfer learning and refinement techniques.

Contribution

The paper proposes a novel pre-training and refinement paradigm for graph partitioning that generalizes to large graphs and improves efficiency without re-training.

Findings

PR-GPT achieves faster graph partitioning on large-scale graphs.

The method maintains high partition quality compared to traditional approaches.

Experiments show significant speedup with minimal quality loss.

Abstract

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.