Decoupled Relation Alignment for Heterogeneous Graph Foundation Models
The recently introduced Decoupled Relation Subspace Alignment (DRSA) framework tackles the difficulties associated with applying Graph Foundation Models (GFMs) to multi-domain heterogeneous graphs (MDHGs). Traditional global feature alignment techniques, such as PCA and SVD, lead to issues like 'Type Collapse' and 'Relation Confusion' due to the alteration of type-specific meanings. By separating feature semantics from relation structures, DRSA employs a dual-relation subspace projection method to facilitate interactions across different types within a common low-rank relation subspace. This plug-and-play solution aims to maintain the integrity of original topologies while promoting effective learning across domains.
Key facts
- DRSA is a plug-and-play relation-driven alignment framework for heterogeneous graphs.
- Existing methods like PCA or SVD cause Type Collapse and Relation Confusion.
- DRSA decouples feature semantics from relation structures.
- It uses a dual-relation subspace projection mechanism.
- The framework coordinates cross-type interactions in a shared low-rank relation subspace.
- The paper is available on arXiv with ID 2605.00731.
- The approach targets multi-domain heterogeneous graphs (MDHGs).
- DRSA aims to preserve original topologies while enabling cross-domain learning.
Entities
Institutions
- arXiv