文件列表:

.ipynb_checkpoints (0, 2023-03-29)
.ipynb_checkpoints\train-checkpoint.py (6645, 2023-03-29)
__pycache__ (0, 2023-03-29)
__pycache__\generator.cpython-39.pyc (3306, 2023-03-29)
__pycache__\model.cpython-39.pyc (3097, 2023-03-29)
__pycache__\utils.cpython-39.pyc (2070, 2023-03-29)
generator.py (6104, 2023-03-29)
hw1_data (0, 2023-03-29)
hw1_data\.DS_Store (6148, 2023-03-29)
hw1_data\Synthetic (0, 2023-03-29)
hw1_data\Synthetic\5000 (0, 2023-03-29)
hw1_data\Synthetic\5000\0.txt (174844, 2023-03-29)
hw1_data\Synthetic\5000\0_score.txt (135222, 2023-03-29)
hw1_data\Synthetic\5000\1.txt (175227, 2023-03-29)
hw1_data\Synthetic\5000\10.txt (174938, 2023-03-29)
hw1_data\Synthetic\5000\10_score.txt (135290, 2023-03-29)
hw1_data\Synthetic\5000\11.txt (174733, 2023-03-29)
hw1_data\Synthetic\5000\11_score.txt (135255, 2023-03-29)
hw1_data\Synthetic\5000\12.txt (174922, 2023-03-29)
hw1_data\Synthetic\5000\12_score.txt (135184, 2023-03-29)
hw1_data\Synthetic\5000\13.txt (174903, 2023-03-29)
hw1_data\Synthetic\5000\13_score.txt (135305, 2023-03-29)
hw1_data\Synthetic\5000\14.txt (174907, 2023-03-29)
hw1_data\Synthetic\5000\14_score.txt (135239, 2023-03-29)
hw1_data\Synthetic\5000\15.txt (175451, 2023-03-29)
hw1_data\Synthetic\5000\15_score.txt (135217, 2023-03-29)
hw1_data\Synthetic\5000\16.txt (175141, 2023-03-29)
hw1_data\Synthetic\5000\16_score.txt (135209, 2023-03-29)
hw1_data\Synthetic\5000\17.txt (174921, 2023-03-29)
hw1_data\Synthetic\5000\17_score.txt (135306, 2023-03-29)
hw1_data\Synthetic\5000\18.txt (175120, 2023-03-29)
hw1_data\Synthetic\5000\18_score.txt (135244, 2023-03-29)
hw1_data\Synthetic\5000\19.txt (174897, 2023-03-29)
hw1_data\Synthetic\5000\19_score.txt (135241, 2023-03-29)
hw1_data\Synthetic\5000\1_score.txt (135186, 2023-03-29)
hw1_data\Synthetic\5000\2.txt (174828, 2023-03-29)
hw1_data\Synthetic\5000\20.txt (174707, 2023-03-29)
... ...

# DrBC-pytorch The implementation of paper named "Learning to Identify High Betweenness Centrality Nodes from Scratch: A Novel Graph Neural Network Approach" (CIKM 2019). ## Project structure * **model.py**: the model structure of DrBC * **utils.py**: the loss and metrics for DrBC training and testing * **train.py**: define how to train the model DrBC * **test.py**: given path of graph and ground truth, and then calculate the testing result * **generator.py**: definition of dataset and how to get information from it, and it also contains the main function to generate the training data, which will store in **train_val_gen/** * **test_30/**: it is a directory for generating the final testing synthetic data (nodes: 5000, 10000, 20000, 50000, 100000) * **test.sh**: shell script for testing all TA's synthetic dataset * **reproduce.ipynb**: notebook for reproducing the paper's tables and figures and some results will store in **reproduce/** directory ## How to use it ### generating your own data ``` python generator.py --batch_size 16 --train_size 10000 --eval_size 100 --scale 0 1 2 3 4 ``` if you want to use the data I generated: click [here](https://drive.google.com/file/d/1Zb2HiBhDZVEtHFHrM4QipIlghBlWD1Cs/view?usp=share_link) ### for training ``` python train.py --scale 100 200 --optimizer adam --lr 1e-4 ``` ### for testing * single file example for testing the com-youtube dataset with GPU ``` python test.py --graph_path hw1_data/youtube/com-youtube.txt \ --bc_path hw1_data/youtube/com-youtube_score.txt \ --model_path model_result/1000_1200.pth --device gpu ``` * hw1_data (multiple files) example for testing all hw1 synthetic 5000 nodes data (and the result will stored in **result.txt**) ``` rm result.txt bash test.sh ``` ## Baseline Comparison * Node2Vec: [PyG Node2Vec](https://pytorch-geometric.readthedocs.io/en/latest/_modules/torch_geometric/nn/models/node2vec.html) * kadraba: [Networkit](https://networkit.github.io/) * RK: [Networkit](https://networkit.github.io/) ## Result of my reproduction ### PCA plotting and graph  ### Training procedure 

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