欺诈Yelp数据集

class dgl.data.FraudYelpDataset(raw_dir=None, random_seed=717, train_size=0.7, val_size=0.1, force_reload=False, verbose=True, transform=None)[source]

Bases: FraudDataset

欺诈Yelp数据集

Yelp数据集包括由Yelp过滤（垃圾邮件）和推荐（合法）的酒店和餐厅评论。可以进行垃圾评论检测任务，这是一个二元分类任务。从<http://dx.doi.org/10.1145/2783258.2783370>中提取的32个手工特征被用作原始节点特征。评论是图中的节点，三种关系是：

  1. R-U-R: 它连接了同一用户发布的评论

  2. R-S-R：它将同一产品下具有相同星级评分（1-5星）的评论连接起来

  3. R-T-R：它连接了同一产品下在同一月份发布的两条评论。

统计：

  • 节点数：45,954

  • 边：

    • R-U-R: 98,630

    • R-T-R: 1,147,232

    • R-S-R: 6,805,486

  • 类：

    • 正面（垃圾邮件）：6,677

    • 负面（合法）：39,277

  • 正负比例: 1 : 5.9

  • 节点特征大小：32

Parameters:

  • raw_dir (str) – Specifying the directory that will store the downloaded data or the directory that already stores the input data. Default: ~/.dgl/

  • random_seed (int) – Specifying the random seed in splitting the dataset. Default: 717

  • train_size (float) – training set size of the dataset. Default: 0.7

  • val_size (float) – validation set size of the dataset, and the size of testing set is (1 - train_size - val_size) Default: 0.1

  • force_reload (bool) – Whether to reload the dataset. Default: False

  • verbose (bool) – Whether to print out progress information. Default: True.

  • transform (callable, optional) – A transform that takes in a DGLGraph object and returns a transformed version. The DGLGraph object will be transformed before every access.

示例

>>> dataset = FraudYelpDataset()
>>> graph = dataset[0]
>>> num_classes = dataset.num_classes
>>> feat = graph.ndata['feature']
>>> label = graph.ndata['label']
__getitem__(idx)

获取图形对象

Parameters:

idx (int) – Item index

Returns:

graph structure, node features, node labels and masks

  • ndata['feature']: node features

  • ndata['label']: node labels

  • ndata['train_mask']: mask of training set

  • ndata['val_mask']: mask of validation set

  • ndata['test_mask']: mask of testing set

Return type:

dgl.DGLGraph

__len__()

数据示例的数量