FIMP: Foundation Model-Informed Message Passing for Graph Neural Networks (2210.09475v5)

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 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.

• The paper introduces the FIMP framework that uses pre-trained foundation model weights as message passing operators in GNNs.
• It employs data-specific node tokenization and cross-node attention to enhance feature representation in graph-structured data.
• Experimental results show performance gains of up to 25.8% in fMRI reconstruction and 17% in image graph tasks compared to baselines.

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

The paper "FIMP: Foundation Model-Informed Message Passing for Graph Neural Networks" introduces a novel framework integrating foundational model capabilities into Graph Neural Networks (GNNs). The Foundation-Informed Message Passing (FIMP) framework addresses a significant gap in leveraging large-scale pre-trained model weights—typically utilized in non-graph domains—within graph-structured settings, enhancing the applicability and performance of GNNs across various domains.

Summary of Contributions

The paper's primary contribution is the development of the FIMP framework, which adapts the weights from transformer-based foundation models as message-passing operators in GNN architectures. This method allows for the utilization of prior knowledge captured by LLMs and other foundational models during pretraining. The adaptation process involves the construction of message-passing operations directly informed by the transformed model weights, applying them to graph-structured data.

Key technical elements of this framework include:

• Node Tokenization: FIMP uses data-specific tokenization schemes to encode node features into sequences suitable for processing by transformer models. This adaptation allows the framework to integrate smoothly with foundation models and effectively utilize their pre-trained weights in new domains.
• Cross-Node Attention: The framework employs cross-node multihead attention, extending beyond traditional self-attention methods by using the destination node's features as the query sequence and the source node's features as the key and value sequences. This approach enables sophisticated message passing between nodes.
• Adaptation to Pretrained Weights: By replacing the native GNN message-passing operations with pre-trained weights from foundation models, the framework enhances the graph's representation quality by incorporating extensive knowledge from prior large-scale unsupervised learning.

Experimental Results and Analysis

The paper evaluates the FIMP framework across multiple domains, including fMRI brain activity reconstruction, gene expression prediction in spatial transcriptomics datasets, and image graph construction from the CIFAR-100 dataset:

• fMRI Brain Activity: The FIMP framework significantly improved the reconstruction accuracy by 25.8% over the nearest baseline, further enhancing performance by utilizing foundation models trained on brain activity like BrainLM.
• Spatial Transcriptomics: FIMP consistently outperformed existing baselines on masked gene expression prediction tasks. The use of pretrained models in limited data settings resulted in further performance gains, illustrating FIMP’s potential in cross-domain applications.
• Image Graph Data: On CIFAR-100, FIMP demonstrated a 17% improvement in reconstruction accuracy compared to traditional GNN approaches, underscoring the utility of integrating foundation model vision capabilities.

These results demonstrate that FIMP can exploit sophisticated representation learned during large-scale pretraining to tackle diverse graph-based tasks effectively. The paper's contributions are especially pertinent in fields relying on complex feature-rich data, where traditional GNNs may struggle to capture nuanced inter-feature relationships.

Implications and Future Directions

The introduction of FIMP opens avenues for integrating domain-agnostic pre-trained models with domain-specific graph tasks, a strategy that could bridge knowledge between distinct modalities. This advance implies a better transfer of insights across disparate areas, does not require bespoke model design for each task, and ensures robust performance despite limited graph structure or node feature specificity.

Potential future developments include enhancing token selection strategies for sparse datasets, integrating efficient attention mechanisms for computational scalability, and incorporating richer context from edge features. An interesting direction might also be the exploration of joint training techniques, leveraging foundation model knowledge while emphasizing specific graph-based task requirements.

In conclusion, this paper presents a compelling approach to expanding the applicability of foundation models beyond traditional domains, providing a robust framework for improving graph neural network capabilities.