| Challenge: | graph neural networks have shown remarkable performance across diverse graph-related tasks, but their high-dimensional hidden representations render them black boxes. |
| Approach: | They propose a graph-based neural network with hidden representations in the form of human-readable text. |
| Outcome: | The proposed GNN outperforms existing LLM-based baseline methods on node classification and link prediction. |
Similar Papers
Let’s Ask GNN: Empowering Large Language Model for Graph In-Context Learning (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Textual Attributed Graphs (TAGs) are crucial for modeling complex real-world systems, yet leveraging large language models (LLMs) for TAGs presents unique challenges due to the gap between sequential text processing and graph-structured data. |
| Approach: | They propose a novel approach that leverages In-Context Learning to integrate graph data and task-specific information into large language models (LLMs) they employ a Graph Neural Network-powered structure-enhanced retriever to select labeled nodes across graphs, incorporating complex graph structures and their supervision signals. |
| Outcome: | Experiments on three tasks and seven LLMs show that AskGNN performs better than existing methods. |
From Nodes to Narratives: Explaining Graph Neural Networks with LLMs and Graph Context (2026.acl-long)
Copied to clipboard
| Challenge: | Existing explanation methods for graph neural networks struggle to generate interpretable, fine-grained rationales. |
| Approach: | They propose a lightweight framework that uses large language models to generate interpretable explanations for GNNs. |
| Outcome: | The proposed framework generates interpretable explanations for GNN predictions using large language models. |
Graph Language Models (2024.acl-long)
Copied to clipboard
| Challenge: | Language Models (LMs) are the workhorses of NLP, but their interplay with structured knowledge graphs (KGs) is still actively researched. |
| Approach: | They propose a Graph Language Model (GLM) that integrates the strengths of both approaches and mitigates their weaknesses. |
| Outcome: | Empirical evaluations show that the proposed model surpasses both LM- and GNN-based baselines in supervised and zero-shot setting, demonstrating their versatility. |
Exploring the Potential of Large Language Models for Heterophilic Graphs (2025.naacl-long)
Copied to clipboard
| Challenge: | Existing approaches for heterophilic graphs overlook rich textual data associated with nodes, which could unlock deeper insights into their heterophilistic contexts. |
| Approach: | They propose a two-stage framework to enhance node classification on heterophilic graphs by leveraging open-world knowledge encoded by large language models. |
| Outcome: | The proposed framework can be used to better characterize heterophilic graphs, where neighboring nodes often exhibit different labels. |
Each graph is a new language: Graph Learning with LLMs (2025.findings-acl)
Copied to clipboard
| Challenge: | Natural language is used to describe graphs, but graph descriptions become verbose and only relying on attribute embeddings limits LLM’s ability to capture adequate graph structural information. |
| Approach: | They propose a graph-defined language for large language model that translates the graph into a corpus instead of graph descriptions and pre-trains LLMs on this corpus to adequately understand the graph. |
| Outcome: | Experiments on five datasets show that the proposed framework outperforms description-based and embedding-based baselines by efficiently modeling different orders of neighbors. |
Digest the Knowledge: Large Language Models empowered Message Passing for Knowledge Graph Question Answering (2025.acl-long)
Copied to clipboard
| Challenge: | Existing methods to augment large language models (LLMs) with external knowledge are unorganized and unorganized. |
| Approach: | They propose a method that learns a concise facts graph and encodes it into multi-level lists of texts to augment LLMs. |
| Outcome: | The proposed method improves on all 5 knowledge graph question answering datasets and offers human-level semantic explainability. |
Large Language Models are Good Relational Learners (2025.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to serialize large language models disregard critical relational structures and creates redundancies. |
| Approach: | They propose a graph neural network encoder to create structured relational prompts for large language models within a retrieval-augmented generation framework. |
| Outcome: | The proposed architecture preserves relational structure of databases while enabling LLMs to process and reason over complex entity relationships. |
Colorful Talks with Graphs: Human-Interpretable Graph Encodings for Large Language Models (2026.findings-acl)
Copied to clipboard
| Challenge: | Graph problems require reasoning over explicit structure, permutation invariance, and computationally complex relationships, creating a mismatch with the representations of text-based models. |
| Approach: | They propose a human-interpretable structural encoding strategy that injects graph structure directly into natural language prompts. |
| Outcome: | The proposed method improves performance on synthetic and real-world datasets. |
Explaining Graph Neural Networks with Large Language Models: A Counterfactual Perspective on Molecule Graphs (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Graph Neural Networks (GNNs) are successful in molecular property prediction tasks, but their outputs are often black-box and not easily understandable by humans. |
| Approach: | They propose a method to unleash the power of large language models (LLMs) to explain GNNs for molecular property prediction. |
| Outcome: | The proposed method uses autoencoder to generate the counterfactual graph topology from a set of counterfact text pairs based on an input graph. |
Language is All a Graph Needs (2024.findings-eacl)
Copied to clipboard
| Challenge: | Existing work on integrating graph problems into generative language modeling framework remains limited. |
| Approach: | They propose an LLM with instructions based on natural language to perform graph tasks. |
| Outcome: | The proposed model surpasses all GNN baselines on ogbn-arxiv, Cora and PubMed datasets and sheds light on generative LLMs as new foundation model for graph machine learning. |