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#	Readme.md

Author: Messi-Q

.idea/GraphDeeSmartContract.iml

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+# GraphDeeSmartContract ![GitHub stars](https://img.shields.io/github/stars/Messi-Q/GraphDeeSmartContract.svg?style=plastic) ![GitHub forks](https://img.shields.io/github/forks/Messi-Q/GraphDeeSmartContract.svg?color=blue&style=plastic) ![License](https://img.shields.io/github/license/Messi-Q/GraphDeeSmartContract.svg?color=blue&style=plastic)
+
+This repo is a python implementation of smart contract vulnerability detection of our DR-GCN network. 
+
+### Running project
+* To run program, use this command: python SmConVulDetector.py.
+* In addition, you can use specific hyper-parameters to train the model. All the hyper-parameters can be found in `parser.py`.
+
+Examples:
+```shell
+python SmConVulDetector.py --dataset training_data/REENTRANCY_CORENODES_1671
+python SmConVulDetector.py --dataset training_data/REENTRANCY_CORENODES_1671 --model gcn_modify --n_hidden 192 --lr 0.001 -f 64,64,64 --dropout 0.1 --vector_dim 100 --epochs 50 --lr_decay_steps 10,20 
+```
+
+Using script：
+Repeating 10 times for different seeds with `train.sh`.
+```shell
+for i in $(seq 1 10);
+do seed=$(( ( RANDOM % 10000 )  + 1 ));
+python SmConVulDetector.py --model gcn_modify --seed $seed | tee logs/smartcheck_"$i".log;
+done
+```
+Then, you can find the training results in the `logs/`.
+
+
+### Dataset
+Original smart contract source code:
+
+Ethereum smart contracts: [Etherscan_contract](https://drive.google.com/open?id=1h9aFFSsL7mK4NmVJd4So7IJlFj9u0HRv)
+
+Vntchain smart contacts: [Vntchain_contract](https://drive.google.com/open?id=1FTb__ERCOGNGM9dTeHLwAxBLw7X5Td4v)
+
+The train data after normalization:
+
+`training_data/LOOP_CORENODES_1317`, `LOOP_FULLNODES_1317`, `REENTRANCY_CORENODES_1671`, `REENTRANCY_FULLNODES_1671`
+
+
+### Reference
+1. The code borrows from [graph_unet](https://github.com/bknyaz/graph_nn)
+2. Thomas N. Kipf, Max Welling, Semi-Supervised Classification with Graph Convolutional Networks, ICLR 2017