HP-JEPA: Hierarchical Partitioning for Multi-Resolution Graph Joint-Embedding Predictive Learning

2026-08-01

AI Summary

Existing graph joint embedding prediction approaches rely on a single predefined partition, which limits their ability to capture multiscale structural information and consequently constrains the expressiveness and generalization of learned representations. This work proposes HP-JEPA, a novel framework that introduces hierarchical, multi-resolution graph partitioning into the JEPA architecture for the first time. HP-JEPA performs context-to-target latent prediction in parallel across multiple scales—from coarse to fine—and adaptively fuses these multiscale representations through task-aware weighting to jointly model local, regional, and global graph structures. Evaluated on eight benchmark datasets, HP-JEPA outperforms the fixed-resolution Graph-JEPA on six of them and demonstrates consistently superior performance across graphs of varying sizes, thereby validating the efficacy and advantages of the proposed multi-resolution strategy.

Abstract

Graph self-supervised learning aims to learn transferable representations from large-scale unlabeled graph data. Joint-embedding predictive architectures (JEPAs) avoid explicit negative-pair construction and raw-input reconstruction by predicting masked targets directly in latent space. However, existing graph JEPAs typically rely on a single predefined graph partition, biasing the learned representations toward one structural granularity and limiting their ability to capture complementary patterns at different graph scales. To address this limitation, we propose HP-JEPA, a hierarchical partitioning framework for multi-resolution graph joint-embedding prediction. HP-JEPA organizes each graph into an ordered bank of coarse-to-fine partition resolutions and performs context-target latent prediction separately at each resolution using an online encoder, an exponential-moving-average target encoder, and a latent predictor. The resulting resolution-specific graph representations are subsequently integrated through concatenation or task-specific resolution weighting, allowing downstream models to combine complementary local, regional, and global structural information. Experiments on seven graph classification benchmarks and one graph regression benchmark show that HP-JEPA outperforms the fixed-resolution Graph-JEPA baseline on 6 of 8 tasks, improving upon Graph-JEPA on most evaluated benchmarks. Size-stratified analyses further show that HP-JEPA achieves higher accuracy than Graph-JEPA in most evaluated graph-size quartiles on three representative datasets. These results highlight the effectiveness of hierarchical multi-resolution partitioning for transferable graph representation learning.

Problem

• graph self-supervised learning
• joint-embedding predictive architectures
• multi-resolution representation
• hierarchical partitioning
• graph representation learning

Innovation

• Hierarchical Partitioning
• Multi-Resolution Learning
• Graph Joint-Embedding
• Self-Supervised Graph Learning
• Latent Space Prediction