Handling Feature Heterogeneity with Learnable Graph Patches

2026-06-16

AI Summary

This work addresses the challenges of feature heterogeneity and cross-domain transfer in graph data caused by the absence of textual information. It proposes “learnable graphlets” as the minimal semantic units of graphs, enabling for the first time a text-free cross-domain graph pre-training framework. By designing graphlet decomposition, a graphlet encoder, and an aggregator, the approach constructs a domain-agnostic architecture that extracts transferable knowledge from multi-domain graph data. The method supports joint pre-training across multiple domains and consistently achieves significant performance gains on diverse downstream tasks and datasets. Moreover, its effectiveness scales with the volume of pre-training data, and it reveals intrinsic connections between graphlet representations, existing graph models, and the transferability of node embeddings.

Abstract

In recent years, the rapid development of foundation models and graph pre-training technologies has spurred increasing interest in constructing a universal pre-trained graph model or Graph Foundation Model (GFM). However, a significant challenge is that existing models are unable to address feature heterogeneity in graph data without textual information, which hinders the transferability of graph models across different datasets. To bridge this gap, we propose the concept of learnable graph patches, which we regard as the smallest semantic units of any graph data. We decompose the graph into learnable graph patches by unfolding the node features and constructing corresponding patch structures separately. We then design a framework that mines transferable information from graph data across domains. Specifically, after extracting graph patches, we propose a patch encoder to extract knowledge from each unit and a patch aggregator to learn how the units are combined into a whole. Due to its domain-agnostic nature, the model can be applied to downstream data across different domains. Furthermore, we analyze the connection between our method and existing graph models, as well as the transferability of the node embeddings it generates. Empirically, our method not only achieves the capability to use multi-domain graphs for pre-training, but also shows enhanced performance across various downstream datasets and tasks. Moreover, we observe consistent improvement in downstream performance as the volume of pre-training data increases.

Problem

• feature heterogeneity
• graph foundation model
• transferability
• graph pre-training
• domain-agnostic

Innovation

• learnable graph patches
• feature heterogeneity
• graph foundation model
• cross-domain transfer
• patch-based representation