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GaLoRA: Parameter-Efficient Graph-Aware LLMs for Node Classification

arXiv:2603.10298v1 Announce Type: new Abstract: The rapid rise of large language models (LLMs) and their ability to capture semantic relationships has led to their adoption in a wide range of applications. Text-attributed graphs (TAGs) are a notable example where LLMs can be combined with Graph Neural Networks to improve the performance of node classification. In TAGs, each node is associated with textual content and such graphs are commonly seen in various domains such as social networks, citation graphs, recommendation systems, etc. Effectively learning from TAGs would enable better representations of both structural and textual representations of the graph and improve decision-making in relevant domains. We present GaLoRA, a parameter-efficient framework that integrates structural information into LLMs. GaLoRA demonstrates competitive performance on node classification tasks with TAGs, performing on par with state-of-the-art models with just 0.24% of the parameter count required by

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Mayur Choudhary, Saptarshi Sengupta, Katerina Potika · · 1 min read · 2 views
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arXiv:2603.10298v1 Announce Type: new Abstract: The rapid rise of large language models (LLMs) and their ability to capture semantic relationships has led to their adoption in a wide range of applications. Text-attributed graphs (TAGs) are a notable example where LLMs can be combined with Graph Neural Networks to improve the performance of node classification. In TAGs, each node is associated with textual content and such graphs are commonly seen in various domains such as social networks, citation graphs, recommendation systems, etc. Effectively learning from TAGs would enable better representations of both structural and textual representations of the graph and improve decision-making in relevant domains. We present GaLoRA, a parameter-efficient framework that integrates structural information into LLMs. GaLoRA demonstrates competitive performance on node classification tasks with TAGs, performing on par with state-of-the-art models with just 0.24% of the parameter count required by full LLM fine-tuning. We experiment with three real-world datasets to showcase GaLoRA's effectiveness in combining structural and semantical information on TAGs.

Executive Summary

GaLoRA, a novel framework, is introduced to integrate structural information into large language models (LLMs) for efficient node classification in text-attributed graphs (TAGs). By leveraging the strengths of both LLMs and Graph Neural Networks, GaLoRA demonstrates competitive performance on node classification tasks, achieving comparable results to state-of-the-art models with significantly reduced parameter counts. This parameter-efficient framework has the potential to improve decision-making in various domains, including social networks, citation graphs, and recommendation systems. This research contributes to the development of more effective and efficient graph-aware LLMs, paving the way for further advancements in natural language processing and machine learning applications.

Key Points

  • ▸ GaLoRA integrates structural information into LLMs for efficient node classification in TAGs
  • ▸ GaLoRA achieves competitive performance on node classification tasks with reduced parameter counts
  • ▸ GaLoRA has the potential to improve decision-making in various domains

Merits

Strength in Parameter Efficiency

GaLoRA's ability to achieve competitive performance with significantly reduced parameter counts makes it a valuable contribution to the field, enabling the development of more efficient graph-aware LLMs.

Improved Decision-Making

By effectively combining structural and semantic information, GaLoRA has the potential to improve decision-making in various domains, including social networks, citation graphs, and recommendation systems.

Advancements in NLP and ML

GaLoRA's research contributes to the development of more effective and efficient graph-aware LLMs, paving the way for further advancements in natural language processing and machine learning applications.

Demerits

Limited Generalizability

The effectiveness of GaLoRA is demonstrated on a limited number of real-world datasets, which may not be representative of a broader range of applications, potentially limiting its generalizability.

Dependence on LLMs

GaLoRA's performance is heavily reliant on the quality and accuracy of the underlying LLMs, which may be a limitation if the LLMs are not well-suited for the specific task or domain.

Expert Commentary

The development of GaLoRA marks a significant advancement in the field of graph-aware LLMs, demonstrating the potential for parameter-efficient frameworks to improve decision-making in various domains. However, the effectiveness of GaLoRA is largely dependent on the quality and accuracy of the underlying LLMs, which may be a limitation in certain applications. Furthermore, the generalizability of GaLoRA's results is limited to the specific datasets used in this study, which may not be representative of a broader range of applications. Nevertheless, the research contributes to the development of more effective and efficient graph-aware LLMs, paving the way for further advancements in natural language processing and machine learning applications.

Recommendations

  • ✓ Future research should focus on expanding the generalizability of GaLoRA's results to a broader range of applications and domains.
  • ✓ Investigations into the potential limitations of GaLoRA, such as its dependence on high-quality LLMs, are warranted to ensure the framework's practical applicability.

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