文件列表:

data/ (0, 2023-11-28)
data/citeseer/ (0, 2023-11-28)
data/citeseer/ind.citeseer.allx (595021, 2023-11-28)
data/citeseer/ind.citeseer.ally (55623, 2023-11-28)
data/citeseer/ind.citeseer.graph (61987, 2023-11-28)
data/citeseer/ind.citeseer.test.index (5000, 2023-11-28)
data/citeseer/ind.citeseer.tx (260321, 2023-11-28)
data/citeseer/ind.citeseer.ty (24135, 2023-11-28)
data/citeseer/ind.citeseer.x (31087, 2023-11-28)
data/citeseer/ind.citeseer.y (3014, 2023-11-28)
data/cora/ (0, 2023-11-28)
data/cora/cora.cites (69928, 2023-11-28)
data/cora/cora.content (7823427, 2023-11-28)
data_load.py (15506, 2023-11-28)
main.py (25468, 2023-11-28)
models.py (23416, 2023-11-28)
requirement.txt (105, 2023-11-28)
test.py (412, 2023-11-28)
utils.py (27320, 2023-11-28)

# GNN-CL Pytorch implementation of paper ['Graph Neural Network with Curriculum Learning for Imbalanced Node Classification.'] submmited to Neurocomputing. ## Dependencies argparse==1.1 numpy==1.24.4 networkx==2.0 scipy==1.6.1 sklearn==0.24.1 torch==1.9.0 python==3.8.8 python main.py --gpu=2 --imbalance --dataset=cora --model=sage --umsample=danger_upsample If any problems occurs via running this code, please contact us at lixiaohe@aircas.ac.cn. Thank you! The code development for this project drew inspiration from GraphSMOTE, for which we extend our special thanks. [GraphSmote](https://github.com/TianxiangZhao/GraphSmote).

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