Awesome-Deep-Graph-Anomaly-Detection 项目教程

Awesome-Deep-Graph-Anomaly-Detection 项目教程

Awesome-Deep-Graph-Anomaly-DetectionAwesome graph anomaly detection techniques built based on deep learning frameworks. Collections of commonly used datasets, papers as well as implementations are listed in this github repository. We also invite researchers interested in anomaly detection, graph representation learning, and graph anomaly detection to join this project as contributors and boost further research in this area.项目地址:https://gitcode.com/gh_mirrors/aw/Awesome-Deep-Graph-Anomaly-Detection

1. 项目的目录结构及介绍

Awesome-Deep-Graph-Anomaly-Detection/
├── data/
│   ├── README.md
│   └── ...
├── models/
│   ├── README.md
│   └── ...
├── utils/
│   ├── README.md
│   └── ...
├── config/
│   ├── config.yaml
│   └── ...
├── main.py
├── README.md
└── ...

目录结构介绍

• data/: 存放项目所需的数据文件。
• models/: 存放各种深度图异常检测模型的实现。
• utils/: 存放项目中使用的各种辅助工具和函数。
• config/: 存放项目的配置文件。
• main.py: 项目的启动文件。
• README.md: 项目的主文档，包含项目的基本介绍和使用说明。

2. 项目的启动文件介绍

main.py

main.py 是项目的启动文件，负责初始化配置、加载数据、训练模型和评估结果。以下是 main.py 的主要功能模块：

import argparse
from config.config import load_config
from data.data_loader import load_data
from models.model_selector import select_model
from utils.trainer import train_model

def main():
    parser = argparse.ArgumentParser(description="Deep Graph Anomaly Detection")
    parser.add_argument("--config", type=str, default="config/config.yaml", help="Path to the config file")
    args = parser.parse_args()

    config = load_config(args.config)
    data = load_data(config)
    model = select_model(config)
    train_model(model, data, config)

if __name__ == "__main__":
    main()

主要功能

• 加载配置: 通过 load_config 函数加载配置文件。
• 加载数据: 通过 load_data 函数加载数据。
• 选择模型: 通过 select_model 函数选择并初始化模型。
• 训练模型: 通过 train_model 函数训练模型并评估结果。

3. 项目的配置文件介绍

config/config.yaml

config.yaml 是项目的配置文件，包含了项目运行所需的各种参数和设置。以下是 config.yaml 的一个示例：

data:
  path: "data/dataset.csv"
  format: "csv"

model:
  name: "GCN"
  parameters:
    learning_rate: 0.01
    epochs: 100
    hidden_dim: 16

train:
  batch_size: 32
  validation_split: 0.2

evaluation:
  metrics: ["accuracy", "f1_score"]

配置项介绍

• data: 数据相关的配置，包括数据路径和格式。
• model: 模型相关的配置，包括模型名称和参数。
• train: 训练相关的配置，包括批次大小和验证集比例。
• evaluation: 评估相关的配置，包括评估指标。

通过修改 config.yaml 文件，可以灵活地调整项目的运行参数和设置。

Awesome-Deep-Graph-Anomaly-DetectionAwesome graph anomaly detection techniques built based on deep learning frameworks. Collections of commonly used datasets, papers as well as implementations are listed in this github repository. We also invite researchers interested in anomaly detection, graph representation learning, and graph anomaly detection to join this project as contributors and boost further research in this area.项目地址:https://gitcode.com/gh_mirrors/aw/Awesome-Deep-Graph-Anomaly-Detection