Introduction to Awesome-Graph-LLM
The Awesome-Graph-LLM project is a comprehensive collection of resources that explore the integration of graph-based techniques with Large Language Models (LLMs). This initiative fills a notable gap in research by focusing on how LLMs, which have achieved substantial success in natural language processing tasks, can be applied to graph structures that are ubiquitous in real-world applications. The repository curates an extensive list of research papers and resources that delve into this intriguing intersection, offering insights into both the current state of research and the myriad possibilities for future exploration.
Datasets, Benchmarks & Surveys
This section collects significant academic endeavors that contribute to the blend of graphs and LLMs. Key papers include those discussing synthetic corpus generation for knowledge-enhanced language model pre-training, the role of LLMs in solving graph problems in natural language, and surveys on graph foundational models, among others. These contributions underscore efforts to understand how LLMs can be extended to grasp complex graph structures.
Prompting
The segment focuses on the methods through which LLMs can be prompted to understand and reason over structured data. Research featured here shows frameworks like StructGPT, which facilitates LLMs to handle structured tasks, and methods to enhance logical reasoning through the "Graph of Thought."
General Graph Model
In this part, the aim is to develop unified models that are versatile enough to apply to diverse classification tasks. Each paper builds upon the concept of aligning graph models with LLM training to handle open-ended tasks effectively. The diversity of approaches, from tuning models with specific graph structures to general frameworks for cross-domain graph learning, reveals the dynamic growth in this area.
Large Multimodal Models (LMMs)
LMMs represent a cutting-edge fusion of graph, text, and other modal inputs to extend the capabilities of LLMs beyond language data. Articles in this section show advancements like GraphAdapter, which optimizes tuning of vision-language models, and GITA, which integrates visual and textual information for enhanced reasoning over graphs.
Applications
Basic Graph Reasoning
This subsection illustrates how LLMs are being infused with graph reasoning capabilities to address traditional graph-related challenges through innovative approaches, such as Graph-ToolFormer, which uses LLMs to enhance graph reasoning through prompt augmentation.
Node Classification
Node classification is a crucial part of graph learning, and deploying LLMs to improve this task is a key research focus. Studies in this category demonstrate the use of LLM-derived features for graph node classification, unveiling methods that do not require labels or those that refine graph features through LLM knowledge.
Graph Classification/Regression
Research here targets the prediction of properties associated with graphs using advanced multimodal analytical methods. The promise of LLMs in molecular property prediction is a highlighted topic.
Knowledge Graph
Knowledge graphs are critical for enhancing LLM understanding. Papers cover a wide array of uses such as integrating knowledge graphs to reduce hallucinations in LLMs or utilizing LLMs for knowledge graph construction and dynamic comprehension.
Resources & Tools
The repository also includes a wide array of tools and resources that can aid researchers and developers in leveraging graph techniques with LLMs. These tools are designed to facilitate experimentation, model enhancement, and real-world application development.
Contributing
The project welcomes contributions from researchers and enthusiasts who wish to expand the repository with new research findings, insights, and practical applications that can substantiate the integration of graph methodologies with LLMs.
Star History
The star history section provides an overview of the repository's popularity and community engagement over time, reflecting its relevance and utility to the broader research community.
Overall, the Awesome-Graph-LLM project serves as a pivotal resource for anyone interested in advancing the capabilities of LLMs in the context of graph-based data structures and real-world applications.