Learning on Graphs has attracted immense attention due to its wide real-world applications. The most popular pipeline for learning on graphs with textual node attributes primarily relies on Graph Neural Networks (GNNs), and utilizes shallow text embedding as initial node representations, which has limitations in general knowledge and profound semantic understanding. In recent years, Large Language Models (LLMs) have been proven to possess extensive common knowledge and powerful semantic comprehension abilities that have revolutionized existing workflows to handle text data. In this paper, we aim to explore the potential of LLMs in graph machine learning, especially the node classification task, and investigate two possible pipelines: LLMs-as-Enhancers and LLMs-as-Predictors. The former leverages LLMs to enhance nodes' text attributes with their massive knowledge and then generate predictions through GNNs. The latter attempts to directly employ LLMs as standalone predictors. We conduct comprehensive and systematical studies on these two pipelines under various settings. From comprehensive empirical results, we make original observations and find new insights that open new possibilities and suggest promising directions to leverage LLMs for learning on graphs. Our codes and datasets are available at https://github.com/CurryTang/Graph-LLM.
Exploring the Adversarial Capabilities of Large Language Models
German Research Center for AI (DFKI)（德国人工智能研究中心）；Technical University of Darmstadt（达姆施塔特工业大学）；DataSpark GmbH（DataSpark有限公司）；Centre for Cognitive Science of Darmstadt（达姆施塔特认知科学中心）；Hessian Center for AI (hessian.AI)（黑森州人工智能中心）
A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs
Department of Computer Science University of California Los Angeles（计算机科学系，加州大学洛杉矶分校）；Bioinformatics IDP University of California Los Angeles（生物信息学IDP，加州大学洛杉矶分校）；Department of Computational Medicine University of California Los Angeles（计算医学系，加州大学洛杉矶分校）
Exploring Hierarchical Molecular Graph Representation in Multimodal LLMs
National University of Singapore（新加坡国立大学）；Institute of High-Performance Computing, Agency for Science, Technology and Research（新加坡科技研究局高性能计算研究所）；Centre for Frontier AI Research, Agency for Science, Technology and Research（新加坡科技研究局前沿人工智能研究中心）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）
Exploring the Potential of Diffusion Large Language Models in Code Generation
College of AI, Tsinghua University（清华大学人工智能学院）；School of Computer Science, Nanjing University（南京大学计算机科学学院）；School of Computer Science and Engineering, Beihang University（北航计算机科学与工程学院）；School of Computer Science, Peking University（北京大学计算机科学学院）
Exploring the Role of Knowledge Graph-Based RAG in Japanese Medical Question Answering with Small-Scale LLMs
The University of Tokyo（东京大学）；NEC Laboratories America（NEC美国实验室）；Dokkyo Medical University（多摩医学大学）；Graduate School of Frontier Sciences, The University of Tokyo（东京大学前沿科学研究生院）
Exploring Conversational Design Choices in LLMs for Pedagogical Purposes: Socratic and Narrative Approaches for Improving Instructor's Teaching Practice
Towards Automated Fact-Checking of Real-World Claims: Exploring Task Formulation and Assessment with LLMs
Technische Universität Berlin（柏林工业大学）；Deutsches Forschungszentrum für Künstliche Intelligenz（德国人工智能研究中心）；National Institute of Informatics（信息学研究所）；Harz University of Applied Sciences（哈尔茨应用技术大学）
Knowledgeable In-Context Tuning: Exploring and Exploiting Factual Knowledge for In-Context Learning
School of Data Science and Engineering, East China Normal University（华东师范大学数据科学与工程学院）；Alibaba Group（阿里巴巴集团）；School of Statistics, East China Normal University（华东师范大学统计学院）
Exploring the Potential of Large Language Models for Improving Digital Forensic Investigation Efficiency
Security Research Group, School of Computer Science , addressline= University College Dublin , city= Belfield , state= Dublin 4 , country= Ireland；organization= School of Criminal Justice , addressline= University of Lausanne , state= Lausanne , country= Switzerland；organization= Present affiliation: Institute of Computer Science , addressline= University of Augsburg , state= Augsburg , country= Germany
Exploring Knowledge Conflicts for Faithful LLM Reasoning: Benchmark and Method
School of Computer Science and Technology, Xi'an Jiaotong University（西安交通大学计算机科学与技术学院）；Department of Computer Science, The University of Manchester（曼彻斯特大学计算机科学系）；Hunan University（湖南大学）；National University of Singapore（新加坡国立大学）
