Add dataset card and link to paper

README.md

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+---
+task_categories:
+- graph-ml
+---
+
+# LoReC: Rethinking Large Language Models for Graph Data Analysis
+
+This repository contains data associated with the paper [LoReC: Rethinking Large Language Models for Graph Data Analysis](https://huggingface.co/papers/2604.17897).
+
+[**GitHub Repository**](https://github.com/Git-King-Zhan/LoReC)
+
+## Introduction
+LoReC (Look, Remember, Contrast) is a novel plug-and-play method for the GraphLLM paradigm. It enhances the understanding of graph data in Large Language Models through three stages: 
+1. **Look**: Redistributing attention to the graph.
+2. **Remember**: Re-injecting graph information into the Feed-Forward Network (FFN).
+3. **Contrast**: Rectifying the vanilla logits produced in the decoding process.
+
+This method improves performance on graph-related tasks without requiring extra fine-tuning.
+
+## Usage
+The graph data in this project is typically stored in PyTorch Geometric (PyG) format within `.pt` files. You can load the data as follows:
+
+```python
+import torch as th
+
+# Load the graph data
+graph_data = th.load('all_graph_data.pt')
+
+# Access specific dataset components (e.g., 'arxiv')
+arxiv_graph = graph_data['arxiv']
+```
+
+## Citation
+```bibtex
+@misc{zhan2026lorecrethinkinglargelanguage,
+      title={LoReC: Rethinking Large Language Models for Graph Data Analysis}, 
+      author={Hongyu Zhan and Qixin Wang and Yusen Tan and Haitao Yu and Jingbo Zhou and Shuai Chen and Jia Li and Xiao Tan and Jun Xia},
+      year={2026},
+      eprint={2604.17897},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2604.17897}, 
+}
+```