LOGIN: A Large Language Model Consulted Graph Neural Network Training Framework

Yiran Qiao, Xiang Ao, Yang Liu, Jiarong Xu, Xiaoqian Sun, Qing He·May 22, 2024

Summary

The paper presents LOGIN, a framework that integrates large language models (LLMs) with graph neural networks (GNNs) to enhance GNN performance on node classification tasks. By using LLMs as consultants, LOGIN crafts prompts for nodes, incorporating semantic and topological information, and refines GNNs through LLM responses. The approach, tested on homophilic and heterophilic graphs, shows that even basic GNNs can outperform advanced models when enhanced with LLMs. LOGIN aims to simplify GNN design and improve effectiveness in real-world applications, demonstrating the potential of LLMs-as-Consultants in graph machine learning.

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the integration of Large Language Models (LLMs) into Graph Neural Network (GNN) training for node classification tasks, specifically focusing on the transductive setting of node classification within a graph . This integration seeks to optimize GNNs by leveraging the consultation of LLMs to enhance the performance of GNNs on diverse graphs with varying characteristics . While the utilization of LLMs in enhancing graph machine learning, particularly GNN capacities, is a novel approach, the fundamental problem of improving node classification within graphs is not new, but the method of using LLM consultation to achieve this goal represents a fresh perspective in the field .


What scientific hypothesis does this paper seek to validate?

The paper aims to explore the integration of Large Language Model (LLM) consultation into Graph Neural Network (GNN) training for the node classification task, specifically focusing on the transductive setting of node classification within a graph . The goal is to leverage LLM consultation to predict the unlabeled nodes in a graph with guidance from labeled nodes, enhancing the performance of GNNs in classifying nodes within the same graph .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper proposes a novel paradigm called "LLMs-as-Consultants" that integrates Large Language Models (LLMs) with Graph Neural Networks (GNNs) in an interactive manner to enhance GNN performance on graph-related tasks . This paradigm involves crafting concise prompts for specific nodes with comprehensive semantic and topological information, which are then used as input to LLMs. The responses from LLMs are utilized to refine GNNs, improving their performance based on the correctness of the LLM responses .

Additionally, the paper introduces a framework named LOGIN (LLM Consulted GNN Training) that implements the "LLMs-as-Consultants" paradigm to enable the interactive utilization of LLMs within the GNN training process . LOGIN aims to streamline the GNN design process and leverage the capabilities of LLMs to enhance GNN performance on various downstream tasks .

Furthermore, the paper discusses the effectiveness of incorporating more nodes in the consultation process with LLMs, showing that consulting LLMs with more nodes can lead to significant performance improvements in GNNs . This approach allows LLMs to provide more insightful responses, refining the model's predictions and decisions, ultimately enhancing GNN performance .

Overall, the paper introduces innovative ideas by leveraging LLMs as consultants to enhance GNN performance, providing a new approach to optimize GNNs for diverse graph structures and characteristics . The "LLMs-as-Consultants" paradigm proposed in the paper integrates Large Language Models (LLMs) with Graph Neural Networks (GNNs) in an interactive manner, offering distinct characteristics and advantages compared to previous methods . This paradigm leverages the unique capabilities of LLMs, such as logical reasoning and open-world knowledge stored in their large-scale parameters, to enhance graph machine learning, particularly the capacities of GNNs . By formulating a new interactive approach that involves crafting concise prompts for specific nodes and refining GNNs based on LLM responses, this paradigm aims to optimize GNN performance on diverse graph structures and characteristics .

In contrast to previous methods like LLMs-as-Predictors and LLMs-as-Enhancers paradigms, the "LLMs-as-Consultants" paradigm consistently outperforms them across various datasets . Specifically, it surpasses the TAPE method equipped with llama2-13b-chat in most cases, demonstrating greater compatibility with lower time and resource consumption . Moreover, experiments show that consulting LLMs with more nodes leads to significant performance improvements in GNNs, highlighting the effectiveness of leveraging LLMs more comprehensively to enhance GNN performance .

The paper also discusses the theoretical and practical aspects of GNN variants tailored for different scenarios, emphasizing the challenges in designing and optimizing these variants over time . By empowering classic GNNs for consistently superior performance on graphs with varying characteristics, the "LLMs-as-Consultants" paradigm offers a streamlined approach to enhance GNN design and performance, potentially matching or surpassing intricately designed GNN variants .

Overall, the "LLMs-as-Consultants" paradigm introduces a novel and effective way to optimize GNNs by integrating LLMs in an interactive manner, showcasing improved performance, lower resource consumption, and the potential to address the challenges associated with designing specialized GNN variants for diverse graph types .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research papers exist in the field of integrating Large Language Models (LLMs) with Graph Neural Networks (GNNs) for enhanced performance. Noteworthy researchers in this area include Binbin Hu, Chuan Shi, Wayne Xin Zhao, Philip S Yu, Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen, Jie Huang, Kevin Chen-Chuan Chang, Mengda Huang, Yang Liu, Xiang Ao, Kuan Li, Jianfeng Chi, Jinghua Feng, Hao Yang, Qing He, among others .

The key to the solution mentioned in the paper involves leveraging the unique and advanced capabilities of LLMs to enhance graph machine learning, particularly the capacities of GNNs. The research aims to empower classic GNNs for consistently superior performance on graphs with varying characteristics by integrating LLMs with GNNs in an interactive manner .


How were the experiments in the paper designed?

The experiments in the paper were designed to evaluate the effectiveness and generalizability of the LOGIN framework on six node classification tasks involving both homophilic and heterophilic graphs . The experiments aimed to demonstrate the capabilities of the proposed LLMs-as-Consultants paradigm, which integrates Large Language Models (LLMs) with Graph Neural Networks (GNNs) in an interactive manner . The study focused on refining GNNs by utilizing responses from LLMs to enhance the performance of GNNs on downstream tasks . The evaluation involved comparing the performance of LOGIN with baselines across different benchmark datasets to showcase the effectiveness of the model .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is Cora, PubMed, and Arxiv-23 . The code used in the study is open source, specifically, Vicuna-v1.5-7b, an LLM consultant, is an open-source LLM trained by fine-tuning Llama 2 on user-shared conversations .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide substantial support for the scientific hypotheses that needed verification. The paper explores the integration of Large Language Models (LLMs) consultation into Graph Neural Network (GNN) training for node classification tasks . The experiments conducted demonstrate the effectiveness of the proposed LOGIN framework on both homophilic and heterophilic graph datasets . The research questions addressed in the evaluation section, such as the performance of the LOGIN framework compared to state-of-the-art GNNs and the contribution of complementary coping mechanisms for LLMs' responses, are thoroughly analyzed .

The paper introduces a new paradigm that integrates LLMs with GNNs in an interactive manner, termed LLMs-as-Consultants, to enhance graph machine learning capabilities . The experiments conducted on various datasets, including homophilic and heterophilic graphs, showcase the versatility and applicability of the proposed LOGIN framework in handling graphs with diverse characteristics . The results obtained from these experiments provide concrete evidence supporting the effectiveness of leveraging LLMs to empower classic GNNs for superior performance on graphs with varying features .

Furthermore, the paper discusses the performance comparison among different models, such as LLMs-as-Predictors, LLMs-as-Enhancers, and LLMs-as-Consultants, highlighting the strengths and contributions of each approach . The detailed experimental setup, including the datasets used, hyperparameters, and evaluation metrics, ensures a robust analysis of the hypotheses and the effectiveness of the proposed LOGIN framework . Overall, the experiments and results presented in the paper offer strong empirical support for the scientific hypotheses under investigation, demonstrating the efficacy of integrating LLM consultation into GNN training for node classification tasks.


What are the contributions of this paper?

The paper makes significant contributions by introducing a new paradigm called "LLMs-as-Consultants" that integrates Large Language Models (LLMs) with Graph Neural Networks (GNNs) in an interactive manner. This paradigm, instantiated in a framework named LOGIN (LLM cOnsulted GNN traINing), enhances the performance of GNNs on downstream tasks by leveraging the advantages of LLMs . The paper focuses on streamlining the GNN design process and utilizing LLMs to improve GNN performance by crafting concise prompts for nodes, incorporating responses from LLMs, and refining GNNs based on the correctness of LLM responses . The empirical evaluation of the LOGIN framework demonstrates its effectiveness in node classification tasks across homophilic and heterophilic graphs .


What work can be continued in depth?

Further research in this field can delve deeper into several aspects:

  • Refining GNN weights during training: Future endeavors could focus on enhancing GNN weights during the training phase by utilizing insights from LLMs' responses, building upon the current research .
  • Efficiently involving large-scale nodes in LLM consultation: Addressing the challenge of efficiently involving a large number of nodes in the LLM consultation process is crucial for scalability and practicality. Exploring methods like distributed computing, optimized query strategies, and hierarchical consultation frameworks could make the paradigm more efficient for real-world applications .
  • Comparing models under different LLM paradigms: Further exploration can involve comparing models under the LLMs-as-Consultants paradigm with LLMs-as-Predictors and LLMs-as-Enhancers to understand their performance differences and potential advantages .
  • Consulting more advanced LLMs: Investigating how consulting more advanced LLMs within the LOGIN framework can unlock greater potential and improve overall performance .
  • Enhancing performance of advanced GNNs: Research could focus on how LOGIN can also help enhance the performance of advanced GNNs, potentially leading to further improvements in graph machine learning tasks .

Introduction
Background
Emergence of large language models (LLMs) in NLP
GNNs limitations in handling complex graph structures
Objective
To develop a novel framework: LOGIN
Improve GNN performance on node classification tasks
Leverage LLMs as consultants for better node representation
Method
Data Collection
Selection of diverse datasets with homophilic and heterophilic graphs
Node and edge data preprocessing for LLM input
Data Integration with LLMs
Prompt Crafting
Semantic information integration
Topological context incorporation
Node-specific prompts for LLMs
LLM Consultation
Querying LLMs for node embeddings and refinements
Extracting relevant information for GNN enhancement
GNN Enhancement
Incorporating LLM responses into GNN architecture
Adaptive learning through LLM-guided updates
Performance Evaluation
Comparative analysis with advanced GNN models
Metrics: accuracy, F1 score, and AUC-ROC
Results and Analysis
Improved performance on node classification tasks
Homophilic and heterophilic graph analysis
Impact of LLMs on model simplicity and effectiveness
Applications and Real-World Considerations
Simplified GNN design for practical use
Potential benefits in various domains (e.g., social networks, chemistry, biology)
Conclusion
LLMs-as-Consultants as a promising direction in graph machine learning
Future research directions and limitations discussed
References
Cited works on LLMs, GNNs, and related hybrid approaches
Basic info
papers
machine learning
artificial intelligence
Advanced features
Insights
What is the primary focus of the LOGIN framework?
How does the integration of LLMs with GNNs enhance node classification in the LOGIN framework?
In what types of graphs (homophilic or heterophilic) did the LOGIN approach show improved performance compared to advanced models?
What is the key advantage of using LLMs as consultants in GNNs, as demonstrated by the paper?

LOGIN: A Large Language Model Consulted Graph Neural Network Training Framework

Yiran Qiao, Xiang Ao, Yang Liu, Jiarong Xu, Xiaoqian Sun, Qing He·May 22, 2024

Summary

The paper presents LOGIN, a framework that integrates large language models (LLMs) with graph neural networks (GNNs) to enhance GNN performance on node classification tasks. By using LLMs as consultants, LOGIN crafts prompts for nodes, incorporating semantic and topological information, and refines GNNs through LLM responses. The approach, tested on homophilic and heterophilic graphs, shows that even basic GNNs can outperform advanced models when enhanced with LLMs. LOGIN aims to simplify GNN design and improve effectiveness in real-world applications, demonstrating the potential of LLMs-as-Consultants in graph machine learning.
Mind map
Extracting relevant information for GNN enhancement
Querying LLMs for node embeddings and refinements
Node-specific prompts for LLMs
Topological context incorporation
Semantic information integration
Metrics: accuracy, F1 score, and AUC-ROC
Comparative analysis with advanced GNN models
Adaptive learning through LLM-guided updates
Incorporating LLM responses into GNN architecture
LLM Consultation
Prompt Crafting
Node and edge data preprocessing for LLM input
Selection of diverse datasets with homophilic and heterophilic graphs
Leverage LLMs as consultants for better node representation
Improve GNN performance on node classification tasks
To develop a novel framework: LOGIN
GNNs limitations in handling complex graph structures
Emergence of large language models (LLMs) in NLP
Cited works on LLMs, GNNs, and related hybrid approaches
Future research directions and limitations discussed
LLMs-as-Consultants as a promising direction in graph machine learning
Potential benefits in various domains (e.g., social networks, chemistry, biology)
Simplified GNN design for practical use
Impact of LLMs on model simplicity and effectiveness
Homophilic and heterophilic graph analysis
Improved performance on node classification tasks
Performance Evaluation
GNN Enhancement
Data Integration with LLMs
Data Collection
Objective
Background
References
Conclusion
Applications and Real-World Considerations
Results and Analysis
Method
Introduction
Outline
Introduction
Background
Emergence of large language models (LLMs) in NLP
GNNs limitations in handling complex graph structures
Objective
To develop a novel framework: LOGIN
Improve GNN performance on node classification tasks
Leverage LLMs as consultants for better node representation
Method
Data Collection
Selection of diverse datasets with homophilic and heterophilic graphs
Node and edge data preprocessing for LLM input
Data Integration with LLMs
Prompt Crafting
Semantic information integration
Topological context incorporation
Node-specific prompts for LLMs
LLM Consultation
Querying LLMs for node embeddings and refinements
Extracting relevant information for GNN enhancement
GNN Enhancement
Incorporating LLM responses into GNN architecture
Adaptive learning through LLM-guided updates
Performance Evaluation
Comparative analysis with advanced GNN models
Metrics: accuracy, F1 score, and AUC-ROC
Results and Analysis
Improved performance on node classification tasks
Homophilic and heterophilic graph analysis
Impact of LLMs on model simplicity and effectiveness
Applications and Real-World Considerations
Simplified GNN design for practical use
Potential benefits in various domains (e.g., social networks, chemistry, biology)
Conclusion
LLMs-as-Consultants as a promising direction in graph machine learning
Future research directions and limitations discussed
References
Cited works on LLMs, GNNs, and related hybrid approaches

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the integration of Large Language Models (LLMs) into Graph Neural Network (GNN) training for node classification tasks, specifically focusing on the transductive setting of node classification within a graph . This integration seeks to optimize GNNs by leveraging the consultation of LLMs to enhance the performance of GNNs on diverse graphs with varying characteristics . While the utilization of LLMs in enhancing graph machine learning, particularly GNN capacities, is a novel approach, the fundamental problem of improving node classification within graphs is not new, but the method of using LLM consultation to achieve this goal represents a fresh perspective in the field .


What scientific hypothesis does this paper seek to validate?

The paper aims to explore the integration of Large Language Model (LLM) consultation into Graph Neural Network (GNN) training for the node classification task, specifically focusing on the transductive setting of node classification within a graph . The goal is to leverage LLM consultation to predict the unlabeled nodes in a graph with guidance from labeled nodes, enhancing the performance of GNNs in classifying nodes within the same graph .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper proposes a novel paradigm called "LLMs-as-Consultants" that integrates Large Language Models (LLMs) with Graph Neural Networks (GNNs) in an interactive manner to enhance GNN performance on graph-related tasks . This paradigm involves crafting concise prompts for specific nodes with comprehensive semantic and topological information, which are then used as input to LLMs. The responses from LLMs are utilized to refine GNNs, improving their performance based on the correctness of the LLM responses .

Additionally, the paper introduces a framework named LOGIN (LLM Consulted GNN Training) that implements the "LLMs-as-Consultants" paradigm to enable the interactive utilization of LLMs within the GNN training process . LOGIN aims to streamline the GNN design process and leverage the capabilities of LLMs to enhance GNN performance on various downstream tasks .

Furthermore, the paper discusses the effectiveness of incorporating more nodes in the consultation process with LLMs, showing that consulting LLMs with more nodes can lead to significant performance improvements in GNNs . This approach allows LLMs to provide more insightful responses, refining the model's predictions and decisions, ultimately enhancing GNN performance .

Overall, the paper introduces innovative ideas by leveraging LLMs as consultants to enhance GNN performance, providing a new approach to optimize GNNs for diverse graph structures and characteristics . The "LLMs-as-Consultants" paradigm proposed in the paper integrates Large Language Models (LLMs) with Graph Neural Networks (GNNs) in an interactive manner, offering distinct characteristics and advantages compared to previous methods . This paradigm leverages the unique capabilities of LLMs, such as logical reasoning and open-world knowledge stored in their large-scale parameters, to enhance graph machine learning, particularly the capacities of GNNs . By formulating a new interactive approach that involves crafting concise prompts for specific nodes and refining GNNs based on LLM responses, this paradigm aims to optimize GNN performance on diverse graph structures and characteristics .

In contrast to previous methods like LLMs-as-Predictors and LLMs-as-Enhancers paradigms, the "LLMs-as-Consultants" paradigm consistently outperforms them across various datasets . Specifically, it surpasses the TAPE method equipped with llama2-13b-chat in most cases, demonstrating greater compatibility with lower time and resource consumption . Moreover, experiments show that consulting LLMs with more nodes leads to significant performance improvements in GNNs, highlighting the effectiveness of leveraging LLMs more comprehensively to enhance GNN performance .

The paper also discusses the theoretical and practical aspects of GNN variants tailored for different scenarios, emphasizing the challenges in designing and optimizing these variants over time . By empowering classic GNNs for consistently superior performance on graphs with varying characteristics, the "LLMs-as-Consultants" paradigm offers a streamlined approach to enhance GNN design and performance, potentially matching or surpassing intricately designed GNN variants .

Overall, the "LLMs-as-Consultants" paradigm introduces a novel and effective way to optimize GNNs by integrating LLMs in an interactive manner, showcasing improved performance, lower resource consumption, and the potential to address the challenges associated with designing specialized GNN variants for diverse graph types .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research papers exist in the field of integrating Large Language Models (LLMs) with Graph Neural Networks (GNNs) for enhanced performance. Noteworthy researchers in this area include Binbin Hu, Chuan Shi, Wayne Xin Zhao, Philip S Yu, Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen, Jie Huang, Kevin Chen-Chuan Chang, Mengda Huang, Yang Liu, Xiang Ao, Kuan Li, Jianfeng Chi, Jinghua Feng, Hao Yang, Qing He, among others .

The key to the solution mentioned in the paper involves leveraging the unique and advanced capabilities of LLMs to enhance graph machine learning, particularly the capacities of GNNs. The research aims to empower classic GNNs for consistently superior performance on graphs with varying characteristics by integrating LLMs with GNNs in an interactive manner .


How were the experiments in the paper designed?

The experiments in the paper were designed to evaluate the effectiveness and generalizability of the LOGIN framework on six node classification tasks involving both homophilic and heterophilic graphs . The experiments aimed to demonstrate the capabilities of the proposed LLMs-as-Consultants paradigm, which integrates Large Language Models (LLMs) with Graph Neural Networks (GNNs) in an interactive manner . The study focused on refining GNNs by utilizing responses from LLMs to enhance the performance of GNNs on downstream tasks . The evaluation involved comparing the performance of LOGIN with baselines across different benchmark datasets to showcase the effectiveness of the model .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is Cora, PubMed, and Arxiv-23 . The code used in the study is open source, specifically, Vicuna-v1.5-7b, an LLM consultant, is an open-source LLM trained by fine-tuning Llama 2 on user-shared conversations .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide substantial support for the scientific hypotheses that needed verification. The paper explores the integration of Large Language Models (LLMs) consultation into Graph Neural Network (GNN) training for node classification tasks . The experiments conducted demonstrate the effectiveness of the proposed LOGIN framework on both homophilic and heterophilic graph datasets . The research questions addressed in the evaluation section, such as the performance of the LOGIN framework compared to state-of-the-art GNNs and the contribution of complementary coping mechanisms for LLMs' responses, are thoroughly analyzed .

The paper introduces a new paradigm that integrates LLMs with GNNs in an interactive manner, termed LLMs-as-Consultants, to enhance graph machine learning capabilities . The experiments conducted on various datasets, including homophilic and heterophilic graphs, showcase the versatility and applicability of the proposed LOGIN framework in handling graphs with diverse characteristics . The results obtained from these experiments provide concrete evidence supporting the effectiveness of leveraging LLMs to empower classic GNNs for superior performance on graphs with varying features .

Furthermore, the paper discusses the performance comparison among different models, such as LLMs-as-Predictors, LLMs-as-Enhancers, and LLMs-as-Consultants, highlighting the strengths and contributions of each approach . The detailed experimental setup, including the datasets used, hyperparameters, and evaluation metrics, ensures a robust analysis of the hypotheses and the effectiveness of the proposed LOGIN framework . Overall, the experiments and results presented in the paper offer strong empirical support for the scientific hypotheses under investigation, demonstrating the efficacy of integrating LLM consultation into GNN training for node classification tasks.


What are the contributions of this paper?

The paper makes significant contributions by introducing a new paradigm called "LLMs-as-Consultants" that integrates Large Language Models (LLMs) with Graph Neural Networks (GNNs) in an interactive manner. This paradigm, instantiated in a framework named LOGIN (LLM cOnsulted GNN traINing), enhances the performance of GNNs on downstream tasks by leveraging the advantages of LLMs . The paper focuses on streamlining the GNN design process and utilizing LLMs to improve GNN performance by crafting concise prompts for nodes, incorporating responses from LLMs, and refining GNNs based on the correctness of LLM responses . The empirical evaluation of the LOGIN framework demonstrates its effectiveness in node classification tasks across homophilic and heterophilic graphs .


What work can be continued in depth?

Further research in this field can delve deeper into several aspects:

  • Refining GNN weights during training: Future endeavors could focus on enhancing GNN weights during the training phase by utilizing insights from LLMs' responses, building upon the current research .
  • Efficiently involving large-scale nodes in LLM consultation: Addressing the challenge of efficiently involving a large number of nodes in the LLM consultation process is crucial for scalability and practicality. Exploring methods like distributed computing, optimized query strategies, and hierarchical consultation frameworks could make the paradigm more efficient for real-world applications .
  • Comparing models under different LLM paradigms: Further exploration can involve comparing models under the LLMs-as-Consultants paradigm with LLMs-as-Predictors and LLMs-as-Enhancers to understand their performance differences and potential advantages .
  • Consulting more advanced LLMs: Investigating how consulting more advanced LLMs within the LOGIN framework can unlock greater potential and improve overall performance .
  • Enhancing performance of advanced GNNs: Research could focus on how LOGIN can also help enhance the performance of advanced GNNs, potentially leading to further improvements in graph machine learning tasks .
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