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+# **Bi-Directional Multi-Scale Graph Dataset Condensation via Information Bottleneck（BiMSGC）**
+
+
+
+This is the official code for AAAI 2025 Bi-Directional Multi-Scale Graph Dataset Condensation via Information Bottleneck
+
+### Requirements
+```
+deeprobust==0.2.9
+gdown==4.7.3
+networkx==3.2.1
+numpy==1.26.3
+ogb==1.3.6
+pandas==2.1.4
+scikit-learn==1.3.2
+scipy==1.11.4
+torch==2.1.2
+torch_geometric==2.4.0
+torch-sparse==0.6.18
+```
+
+## Download Datasets
+For Citeseer Pubmed and Squirrel, the code will directly download them.
+For Reddit, Flickr, and Ogbn-arXiv, we use the datasets provided by [GraphSAINT](https://github.com/GraphSAINT/GraphSAINT). They are available on [Google Drive link](https://drive.google.com/open?id=1zycmmDES39zVlbVCYs88JTJ1Wm5FbfLz) (the links are provided by GraphSAINT team). 
+Download the files and unzip them to `data` at the root directory. 
+
+## Instructions
+
+(1) Run preprocess.py to preprocess the dataset and conduct the spectral decomposition.
+
+(2) Initialize node features of the synthetic graph by running feat_init.py.
+
+(3) Distill the synthetic graph by running distill.py.
+
+## Cite
+Welcome to kindly cite our work!
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