Our software paper and benchmark paper are publicly available. If you use PyGOD or BOND in a scientific publication, we would appreciate citations to the following papers:

  author  = {Kay Liu and Yingtong Dou and Xueying Ding and Xiyang Hu and Ruitong Zhang and Hao Peng and Lichao Sun and Philip S. Yu},
  title   = {{PyGOD}: A {Python} Library for Graph Outlier Detection},
  journal = {Journal of Machine Learning Research},
  year    = {2024},
  volume  = {25},
  number  = {141},
  pages   = {1--9},
  url     = {}
 author = {Liu, Kay and Dou, Yingtong and Zhao, Yue and Ding, Xueying and Hu, Xiyang and Zhang, Ruitong and Ding, Kaize and Chen, Canyu and Peng, Hao and Shu, Kai and Sun, Lichao and Li, Jundong and Chen, George H and Jia, Zhihao and Yu, Philip S},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {S. Koyejo and S. Mohamed and A. Agarwal and D. Belgrave and K. Cho and A. Oh},
 pages = {27021--27035},
 publisher = {Curran Associates, Inc.},
 title = {{BOND}: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs},
 url = {},
 volume = {35},
 year = {2022}


Liu, K., Dou, Y., Ding, X., Hu, X., Zhang, R., Peng, H., Sun, L. and Yu, P.S., 2024. PyGOD: A Python library for graph outlier detection. Journal of Machine Learning Research, 25(141), pp.1-9.
Liu, K., Dou, Y., Zhao, Y., Ding, X., Hu, X., Zhang, R., Ding, K., Chen, C., Peng, H., Shu, K., Sun, L., Li, J., Chen, G.H., Jia, Z., and Yu, P.S., 2022. BOND: Benchmarking unsupervised outlier node detection on static attributed graphs. Advances in Neural Information Processing Systems, 35, pp.27021-27035.