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.
King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）；SDAIA-KAUST Center of Excellence in Data Science and Artificial Intelligence（沙特数据与人工智能局-阿卜杜拉国王科技大学数据科学与人工智能卓越中心）；Iowa State University（爱荷华州立大学）；Computational Bioscience Research Center（计算生物科学研究中心）
RIDGECUT: Learning Graph Partitioning with Rings and Wedges
College of Computer Science and Artificial Intelligence, Shanghai Key Laboratory of Data Science（计算机科学与人工智能学院，上海数据科学重点实验室）；University of Illinois Chicago（伊利诺伊大学芝加哥分校）；PayPal Inc.（PayPal公司）；Center for AI Safety（人工智能安全中心）；Qatar Computing Research Institute（卡塔尔计算研究所）；Hamad Bin Khalifa University（哈马德·本·卡西姆大学）；Computing and Mathematical Sciences (CMS) Division（计算与数学科学（CMS）部门）；Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)（Mohamed bin Zayed人工智能大学（MBZUAI））；Fudan University（复旦大学）
InfraredGP: Efficient Graph Partitioning via Spectral Graph Neural Networks with Negative Corrections
Department of Strategic and Advanced Interdisciplinary Research, Pengcheng Laboratory (PCL)（战略与先进跨学科研究部，鹏城实验室）；LMIB, NLSDE, BDBC, and School of Artificial Intelligence, Beihang University（LMIB、NLSDE、BDBC及北京航空航天大学人工智能学院）；Hangzhou International Innovation Institute, Beihang University（杭州国际创新研究院，北京航空航天大学）；Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University（环境健康科学系，麦金尼公共卫生学院，哥伦比亚大学）
