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# README | ||
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This repository contains a implementation of our "WalkLM: A Uniform Language Model Fine-tuning Framework for Attributed Graph Embedding" . | ||
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## Environment Setup | ||
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1. Pytorch 1.12.1 | ||
2. Python 3.7.15 | ||
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### Run | ||
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The implementation of embedding generate (```emb.py```)、node classification task(```nc.py```) and link prediction task (```lp.py```); | ||
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## Example to run the codes | ||
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### step 1: fine-tune language model and generate embeddings | ||
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```python | ||
python emb.py | ||
``` | ||
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### step 2: node classification task | ||
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```python | ||
python nc.py | ||
``` | ||
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### step 3: link prediction task | ||
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```python | ||
python lp.py | ||
``` | ||
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## Citation | ||
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If you find the code useful, please consider citing the following paper: | ||
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``` | ||
@inproceedings{tan2023walklm, | ||
title={WalkLM: A Uniform Language Model Fine-tuning Framework for Attributed Graph Embedding}, | ||
author={Tan, Yanchao and Zhou, Zihao and Lv, Hang and Liu, Weiming and Yang, Carl}, | ||
booktitle={Thirty-seventh Conference on Neural Information Processing Systems}, | ||
year={2023} | ||
} | ||
``` | ||
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