Data Repository for PyGOD

The statistics of the available dataset (#Con. means the number of contextual outliers, while #Strct. means the number of structural outliers. The number of outliers is slightly less than the sum of two types of outliers because of the intersection between two types of outliers.):

| Dataset | Type | #Nodes | #Edges | #Feat | Avg. Degree | #Con. | #Strct. | #Outliers | Outlier Ratio | | ------------ | --------- | ------ | ------- | ------ | ----------- | ----- | ------- | --------- | ------------- | | 'weibo' | organic | 8,405 | 407,963 | 400 | 48.5 | - | - | 868 | 10.3% | | 'reddit' | organic | 10,984 | 168,016 | 64 | 15.3 | - | - | 366 | 3.3% | | 'disney' | organic | 124 | 335 | 28 | 2.7 | - | - | 6 | 4.8% | | 'books' | organic | 1,418 | 3,695 | 21 | 2.6 | - | - | 28 | 2.0% | | 'enron' | organic | 13,533 | 176,987 | 18 | 13.1 | - | - | 5 | 0.04% | | 'injcora' | injected | 2,708 | 11,060 | 1,433 | 4.1 | 70 | 70 | 138 | 5.1% | | 'injamazon' | injected | 13,752 | 515,042 | 767 | 37.2 | 350 | 350 | 694 | 5.0% | | 'injflickr' | injected | 89,250 | 933,804 | 500 | 10.5 | 2,240 | 2,240 | 4,414 | 4.9% | | 'gentime' | generated | 1,000 | 5,746 | 64 | 5.7 | 100 | 100 | 189 | 18.9% | | 'gen100' | generated | 100 | 618 | 64 | 6.2 | 10 | 10 | 18 | 18.0% | | 'gen500' | generated | 500 | 2,662 | 64 | 5.3 | 10 | 10 | 20 | 4.0% | | 'gen1000' | generated | 1,000 | 4,936 | 64 | 4.9 | 10 | 10 | 20 | 2.0% | | 'gen5000' | generated | 5,000 | 24,938 | 64 | 5.0 | 10 | 10 | 20 | 0.4% | | 'gen_10000' | generated | 10,000 | 49,614 | 64 | 5.0 | 10 | 10 | 20 | 0.2% |

To use the datasets:

python from pygod.utils import load_data data = load_data('weibo') # in PyG format Alternative download source in Baidu Disk (Chinese): https://pan.baidu.com/s/1afEZaygCRUYWJPtVbzuRYw Access Code: bond

For injected/generated datasets, the labels meanings are as follows.

• 0: inlier
• 1: contextual outlier only
• 2: structural outlier only
• 3: both contextual outlier and structural outlier

Examples to convert the labels are as follows:

python y = data.y.bool() # binary labels (inlier/outlier) yc = data.y >> 0 & 1 # contextual outliers ys = data.y >> 1 & 1 # structural outliers