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GLOD-Issues

Source code and additional results for On Using Classification Datasets to Evaluate Graph Outlier Detection.

Full Results

Results over 10 datasets and 12 GLOD detectors

Run Models

Kernels (WL, PK)

from loader import *
from kernel import *
data_name = 'DD'
dataset = load_data(data_name)[2] 
loader = DataLoader(dataset, batch_size=1, shuffle=False)
model = KernelBasedGLAD(kernel='WL', detector='LOF', WL_iter=5, PK_bin_width=0.1)
model.fit(loader) 

Embeddings (Graph2Vec, FGSD)

from loader import *
from embedder import *
data_name = 'DD'
dataset = load_data(data_name)[2] 
loader = DataLoader(dataset, batch_size=1, shuffle=False)
model = EmbeddingBasedGLAD(embedder='FGSD', detector='LOF', G2V_wl_iter=3, normalize_embedding=False)
model.fit(loader)

OCGIN

from loader import *
from ocgin import *
data_name = 'DD'
loaders = create_loaders(data_name, batch_size=32)
model = OCGIN(loaders[3][0].num_features, weight_decay=5e-4, nlayer=5)
trainer = pl.Trainer(gpus=1, max_epochs=25, logger=False,  weights_summary=None)
trainer.fit(loaders[0])
trainer.test(test_dataloaders=loaders[2])[0]

Cite

Please cite our paper if you use the code.

@article{zhao2021glod-issues,
  title={On using classification datasets to evaluate graph outlier detection: Peculiar observations and new insights},
  author={Zhao, Lingxiao and Akoglu, Leman},
  journal={Big Data},
  year={2021},
  publisher={Mary Ann Liebert, Inc., publishers 140 Huguenot Street, 3rd Floor New~…}
}

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Source code and additional results for GLOD issues

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