A PyTorch implementation of "[Toward Reasoning on the Boundary: A Mixup-based Approach for Graph Anomaly Detection]"

Dependencies for all experiments of ANOMIX are as follows:

• Python == 3.7.8

• PyTorch == 1.6.0

• NetworkX == 2.6.3

• Scikit-learn == 0.23.2

• NumPy == 1.18.5

• SciPy == 1.7.3

• DGL == 0.4.1

All of datasets used in this paper are put in ./dataset folder and graph information (e.g., adjacency, attribute, and label) is included in each dataset file (.mat).

python run.py --dataset cora