Graph Positional and Structural Encoder
This addresses a fundamental problem in graph neural networks for researchers and practitioners, offering a more efficient and transferable alternative to existing methods, though it appears incremental as it builds on prior PSE and GNN work.
The paper tackles the challenge of designing optimal positional and structural encodings (PSEs) for graph prediction tasks by introducing GPSE, a graph encoder that learns a common latent representation for multiple PSEs, which significantly outperforms explicitly computed PSEs across benchmarks and matches them in others.
Positional and structural encodings (PSE) enable better identifiability of nodes within a graph, rendering them essential tools for empowering modern GNNs, and in particular graph Transformers. However, designing PSEs that work optimally for all graph prediction tasks is a challenging and unsolved problem. Here, we present the Graph Positional and Structural Encoder (GPSE), the first-ever graph encoder designed to capture rich PSE representations for augmenting any GNN. GPSE learns an efficient common latent representation for multiple PSEs, and is highly transferable: The encoder trained on a particular graph dataset can be used effectively on datasets drawn from markedly different distributions and modalities. We show that across a wide range of benchmarks, GPSE-enhanced models can significantly outperform those that employ explicitly computed PSEs, and at least match their performance in others. Our results pave the way for the development of foundational pre-trained graph encoders for extracting positional and structural information, and highlight their potential as a more powerful and efficient alternative to explicitly computed PSEs and existing self-supervised pre-training approaches. Our framework and pre-trained models are publicly available at https://github.com/G-Taxonomy-Workgroup/GPSE. For convenience, GPSE has also been integrated into the PyG library to facilitate downstream applications.