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AI Research
arXiv CS.AI·Jun 10·5 min read
ERAlign: Energy-based Representation Alignment of GNNs and LLMs on Text-attributed Graphs
TL;DR
Integrating Graph Neural Networks (GNNs) and Large Language Models (LLMs) on Text-attributed Graphs (TAGs) has been challenging due to poorly aligned representations. The proposed Energy-based Representation Alignment (ERAlign) framework uses Energy-based Models (EBMs) to align GNN and LLM outputs in a shared latent space, optimizing representation consistency.
✦ Why It Matters
Engineers can leverage ERAlign to improve model performance on tasks involving complex graph and text data.
Key Takeaways
- Utilize ERAlign for better representation alignment in TAGs.
- Implement Energy-based Models to enhance GNN and LLM integration.
- Focus on layer-wise alignment for improved model performance.
- Explore Energy Discrepancy to reduce training costs and improve efficiency.
Full Summary
Text-attributed Graphs (TAGs) combine textual information with graph structures, enabling rich semantic representation. However, aligning the outputs of Graph Neural Networks (GNNs) and Large Language Models (LLMs) has proven difficult, often resulting in representation drift and limited generalization.
To address this, the Energy-based Representation Alignment (ERAlign) framework was developed, which employs Energy-based Models (EBMs) to project GNN-encoded graph structures and LLM-derived text embeddings into a shared latent space. This approach optimizes alignment through a distance metric and an EBM objective, reducing energy values to achieve better representation alignment.
The introduction of Energy Discrepancy (ED) enhances training efficiency by minimizing sampling costs and ensuring theoretical guarantees. Evaluations on eight TAG datasets demonstrated that ERAlign outperforms existing methods, achieving state-of-the-art results in both supervised and cross-task scenarios.
These findings suggest significant improvements in the integration of GNNs and LLMs for complex data structures.