first文件夹是算法最早的版本
使用数据集1592993485_noise.pcap
调用已有库进行提取即可
由features_extraction.py实现
使用python复现了上述代码，由于需要适配后面的graph2vec算法的数据结构，对上述算法的逻辑进行了一定的改进。
分析g2v的数据结构：
{
"edges":
[[11, 10], [11, 13], [11, 7], [10, 12], [10, 5], [13, 14], [12, 15], [15, 14], [14, 9], [17, 19], [17, 8], [16, 8], [16, 6], [16, 18], [19, 20], [19, 4], [1, 7], [0, 7], [3, 9], [2, 9], [5, 6]],
"features":
{"0": "1", "1": "1", "2": "1", "3": "1", "4": "1", "5": "2", "6": "2", "7": "2", "8": "2", "9": "2", "10": "3", "11": "3", "12": "3", "13": "3", "14": "3", "15": "3", "16": "3", "17": "3", "18": "3", "19": "3", "20": "3"}
}边是由节点编号组成，特征表示节点的值，据此作出如下改动:
-
预处理时进行编码，然后将符号表示在编码上，然后构建边
-
将节点特征直接表示在features特性中
最终导出json文件
由graph_construction.py实现
文件夹是src,dataset,features
--input-path STR Input folder. Default is `dataset/`.
--output-path STR Embeddings path. Default is `features/nci1.csv`.
example
$ python src/graph2vec.py --input-path new_data/ --output-path features/nci2.csv
本实验中
$ python src/graph2vec.py --input-path json/ --output-path features/nci2.csv
--dimensions INT Number of dimensions. Default is 128.
--workers INT Number of workers. Default is 4.
--epochs INT Number of training epochs. Default is 1.
--min-count INT Minimal feature count to keep. Default is 5.
--wl-iterations INT Number of feature extraction recursions. Default is 2.
--learning-rate FLOAT Initial learning rate. Default is 0.025.
--down-sampling FLOAT Down sampling rate for frequent features. Default is 0.0001.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent