  Foundation models have achieved remarkable success across many domains,
relying on pretraining over vast amounts of data. Graph-structured data often
lacks the same scale as unstructured data, making the development of graph
foundation models challenging. In this work, we propose Foundation-Informed
Message Passing (FIMP), a Graph Neural Network (GNN) message-passing framework
that leverages pretrained non-textual foundation models in graph-based tasks.
We show that the self-attention layers of foundation models can effectively be
repurposed on graphs to perform cross-node attention-based message-passing. Our
model is evaluated on a real-world image network dataset and two biological
applications (single-cell RNA sequencing data and fMRI brain activity
recordings) in both finetuned and zero-shot settings. FIMP outperforms strong
baselines, demonstrating that it can effectively leverage state-of-the-art
foundation models in graph tasks.
