Papers with graph machine learning
Language is All a Graph Needs (2024.findings-eacl)
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| 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. |
LGA: LLM-GNN Aggregation for Temporal Evolution Attribute Graph Prediction (2025.emnlp-main)
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| Challenge: | Current methods focus on 1-hop neighborhood aggregation, lacking capability to capture complex structural interactions. |
| Approach: | They propose a framework that integrates structural information into attribute embeddings through an attribute embedded loss. |
| Outcome: | The proposed framework shows significant improvements over existing methods on real-world datasets. |