8 papers
Clustering as Reasoning: A -Means Interpretation of Chain-of-Thought Graph Learning
Xuanting Xie, Zhaochen Guo, Bingheng Li +4
Chain-of-Thought (CoT) prompting has shown promise in enhancing the reasoning capabilities of large language models (LLMs) on text-attributed graphs (TAGs). This work reframes CoT-…
HoReN: Normalized Hopfield Retrieval for Large-Scale Sequential Model Editing
Yuan Fang, Yi Xie, Xuming Ran
Large language models encode vast factual knowledge that can become outdated or incorrect after deployment, yet retraining is prohibitively costly. This motivates lifelong model ed…
HeTGB: A Comprehensive Benchmark for Heterophilic Text-Attributed Graphs
Shujie Li, Yuxia Wu, Yuan Fang +1
Graph neural networks (GNNs) have demonstrated success in modeling relational data primarily under the assumption of homophily. However, many real-world graphs exhibit heterophily,…
CDW-CoT: Clustered Distance-Weighted Chain-of-Thoughts Reasoning
Yuanheng Fang, Guoqing Chao, Wenqiang Lei +2
Large Language Models (LLMs) have recently achieved impressive results in complex reasoning tasks through Chain of Thought (CoT) prompting. However, most existing CoT methods rely…
A Survey of Ontology Expansion for Conversational Understanding
Jinggui Liang, Yuxia Wu, Yuan Fang +2
In the rapidly evolving field of conversational AI, Ontology Expansion (OnExp) is crucial for enhancing the adaptability and robustness of conversational agents. Traditional models…
Retrieval Augmented Generation for Dynamic Graph Modeling
Yuxia Wu, Lizi Liao, Yuan Fang
Modeling dynamic graphs, such as those found in social networks, recommendation systems, and e-commerce platforms, is crucial for capturing evolving relationships and delivering re…
Exploring the Potential of Large Language Models for Heterophilic Graphs
Yuxia Wu, Shujie Li, Yuan Fang +1
Large language models (LLMs) have presented significant opportunities to enhance various machine learning applications, including graph neural networks (GNNs). By leveraging the va…
On the Feasibility of Simple Transformer for Dynamic Graph Modeling
Yuxia Wu, Yuan Fang, Lizi Liao
Dynamic graph modeling is crucial for understanding complex structures in web graphs, spanning applications in social networks, recommender systems, and more. Most existing methods…