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基于浏览记录的个性化节点推荐算法

图卷积模型代码采用Thomas等人的工作,GitHub:https://github.com/tkipf/gcn

实验环境

  • python==3.6.13
  • tensorflow==1.10

目录结构

├─ gcn-master
│  ├─ experiment
│  │  ├─ cora_rank(保存各类算法在某数据集上的排序结果)
│  │  ├─ ...
│  │  ├─ draw(画结果图的py文件)
│  │  ├─ exp.py(对比各类排序算法的排序结果)
│  │  └─ run.py(加载预测模型并进行节点排序,将结果保存)
│  │
│  ├─ gcn
│  │  ├─ data(数据集)
│  │  ├─ ...
│  │  └─ train.py(加载数据集训练模型并保存模型)
│  │  
│  model
│  │  ├─ ...
│  │  └─ cora(在某数据集上的模型)
│  │ 
│  └─exp_result.docx(实验结果) 
  • 首先运行gcn/train.py,训练模型并保存至model文件夹。
  • 运行experiment/run.py加载模型进行节点排序并保存排序结果。
  • 运行experiment/exp.py对比算法的排序结果

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