LLM-Enhanced Energy Contrastive Learning for Out-of-Distribution Detection in Text-Attributed Graphs
arXiv:2603.20293v1 Announce Type: new Abstract: Text-attributed graphs, where nodes are enriched with textual attributes, have become a powerful tool for modeling real-world networks such as citation, social, and transaction networks. However, existing methods for learning from these graphs often assume that the distributions of training and testing data are consistent. This assumption leads to significant performance degradation when faced with out-of-distribution (OOD) data. In this paper, we address the challenge of node-level OOD detection in text-attributed graphs, with the goal of maintaining accurate node classification while simultaneously identifying OOD nodes. We propose a novel approach, LLM-Enhanced Energy Contrastive Learning for Out-of-Distribution Detection in Text-Attributed Graphs (LECT), which integrates large language models (LLMs) and energy-based contrastive learning. The proposed method involves generating high-quality OOD samples by leveraging the semantic under
arXiv:2603.20293v1 Announce Type: new Abstract: Text-attributed graphs, where nodes are enriched with textual attributes, have become a powerful tool for modeling real-world networks such as citation, social, and transaction networks. However, existing methods for learning from these graphs often assume that the distributions of training and testing data are consistent. This assumption leads to significant performance degradation when faced with out-of-distribution (OOD) data. In this paper, we address the challenge of node-level OOD detection in text-attributed graphs, with the goal of maintaining accurate node classification while simultaneously identifying OOD nodes. We propose a novel approach, LLM-Enhanced Energy Contrastive Learning for Out-of-Distribution Detection in Text-Attributed Graphs (LECT), which integrates large language models (LLMs) and energy-based contrastive learning. The proposed method involves generating high-quality OOD samples by leveraging the semantic understanding and contextual knowledge of LLMs to create dependency-aware pseudo-OOD nodes, and applying contrastive learning based on energy functions to distinguish between in-distribution (IND) and OOD nodes. The effectiveness of our method is demonstrated through extensive experiments on six benchmark datasets, where our method consistently outperforms state-of-the-art baselines, achieving both high classification accuracy and robust OOD detection capabilities.
Executive Summary
This article proposes a novel approach, LLM-Enhanced Energy Contrastive Learning for Out-of-Distribution Detection in Text-Attributed Graphs (LECT), to address the challenge of node-level out-of-distribution (OOD) detection in text-attributed graphs. The method integrates large language models (LLMs) and energy-based contrastive learning to generate high-quality OOD samples and distinguish between in-distribution (IND) and OOD nodes. The authors demonstrate the effectiveness of their method through extensive experiments on six benchmark datasets, achieving both high classification accuracy and robust OOD detection capabilities. The proposed approach has the potential to significantly improve the performance of node classification in text-attributed graphs, particularly in real-world applications where OOD data is common.
Key Points
- ▸ LLM-Enhanced Energy Contrastive Learning (LECT) is proposed for node-level OOD detection in text-attributed graphs.
- ▸ The method integrates LLMs and energy-based contrastive learning to generate high-quality OOD samples.
- ▸ The authors demonstrate the effectiveness of LECT through extensive experiments on six benchmark datasets.
Merits
Strength
The proposed method effectively integrates LLMs and energy-based contrastive learning to address the challenge of OOD detection in text-attributed graphs.
Demerits
Limitation
The method relies on the availability of high-quality LLMs and energy-based contrastive learning models, which may not be universally accessible.
Expert Commentary
The article makes a significant contribution to the field of machine learning, particularly in the area of text-attributed graphs. The proposed method is well-motivated and effectively addresses the challenge of OOD detection in text-attributed graphs. However, the method's reliance on high-quality LLMs and energy-based contrastive learning models may limit its accessibility. Additionally, the evaluation of the method is limited to six benchmark datasets, and further experiments on more diverse datasets are necessary to fully evaluate the method's performance. Overall, the article is well-written and demonstrates a clear understanding of the research question and the proposed solution.
Recommendations
- ✓ Future research should focus on developing methods to improve the accessibility of high-quality LLMs and energy-based contrastive learning models.
- ✓ Further experiments should be conducted on more diverse datasets to evaluate the method's performance in different settings.
Sources
Original: arXiv - cs.AI
