FIMP: Foundation Model-Informed Message Passing for Graph Neural Networks

Syed A Rizvi, Nazreen Pallikkavaliyaveetil MohammedSheriff, David Zhang, Zhuoyang Lyu, Nhi Nguyen, Haoran Lyu, Benjamin W Christensen, Josue Ortega Caro, Antonio Henrique de Oliveira Fonseca, Emanuele Zappala, Maryam Bagherian, Christopher Lee Averill, Chadi Abdallah, Amin Karbasi, Rex Ying, Maria Brbic, Rahul Madhav Dhodapkar, David van Dijk

Keywords: Graph Neural Networks, Message-Passing, Foundation Models

TL;DR: We propose Foundation-Informed Message Passing (FIMP), a message-passing framework that leverages non-textual foundation models pretrained on unstructured data to create messages between nodes in a graph.

Abstract: 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 repurposes existing pretrained non-textual foundation models for graph-based tasks. We show that the self-attention layers of foundation models can effectively be leveraged on graphs to perform cross-node attention-based message-passing. Our model is evaluated across diverse domains on image networks, single-cell RNA sequencing, and fMRI brain activity recordings in finetuned and zero-shot settings. FIMP outperforms strong baselines, demonstrating that it can effectively leverage state-of-the-art foundation models in graph tasks.

Primary Area: learning on graphs and other geometries & topologies

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Submission Number: 7785