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[BLM19]

Sambaran Bandyopadhyay, N Lokesh, and M Narasimha Murty. Outlier aware network embedding for attributed networks. In Proceedings of the AAAI conference on artificial intelligence, volume 33, 12–19. 2019.

[BVM20]

Sambaran Bandyopadhyay, Saley Vishal Vivek, and MN Murty. Outlier resistant unsupervised deep architectures for attributed network embedding. In Proceedings of the 13th International Conference on Web Search and Data Mining, 25–33. 2020.

[CCL+21]

Lei Cai, Zhengzhang Chen, Chen Luo, Jiaping Gui, Jingchao Ni, Ding Li, and Haifeng Chen. Structural temporal graph neural networks for anomaly detection in dynamic graphs. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management, 3747–3756. 2021.

[CLW+20]

Zhenxing Chen, Bo Liu, Meiqing Wang, Peng Dai, Jun Lv, and Liefeng Bo. Generative adversarial attributed network anomaly detection. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management, 1989–1992. 2020.

[DLBL19]

Kaize Ding, Jundong Li, Rohit Bhanushali, and Huan Liu. Deep anomaly detection on attributed networks. In Proceedings of the 2019 SIAM International Conference on Data Mining, 594–602. SIAM, 2019.

[DLS+20]

Yingtong Dou, Zhiwei Liu, Li Sun, Yutong Deng, Hao Peng, and Philip S Yu. Enhancing graph neural network-based fraud detectors against camouflaged fraudsters. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management, 315–324. 2020.

[FZL20]

Haoyi Fan, Fengbin Zhang, and Zuoyong Li. Anomalydae: dual autoencoder for anomaly detection on attributed networks. In ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 5685–5689. 2020. doi:10.1109/ICASSP40776.2020.9053387.

[KW16]

Thomas N Kipf and Max Welling. Variational graph auto-encoders. arXiv preprint arXiv:1611.07308, 2016.

[KKSZ11]

Hans-Peter Kriegel, Peer Kroger, Erich Schubert, and Arthur Zimek. Interpreting and unifying outlier scores. In Proceedings of the 2011 SIAM International Conference on Data Mining, 13–24. SIAM, 2011.

[LDHL17]

Jundong Li, Harsh Dani, Xia Hu, and Huan Liu. Radar: residual analysis for anomaly detection in attributed networks. In IJCAI, 2152–2158. 2017.

[LLP+21]

Yixin Liu, Zhao Li, Shirui Pan, Chen Gong, Chuan Zhou, and George Karypis. Anomaly detection on attributed networks via contrastive self-supervised learning. IEEE transactions on neural networks and learning systems, 2021.

[PLL+18]

Zhen Peng, Minnan Luo, Jundong Li, Huan Liu, and Qinghua Zheng. Anomalous: a joint modeling approach for anomaly detection on attributed networks. In IJCAI, 3513–3519. 2018.

[PVD20]

Lorenzo Perini, Vincent Vercruyssen, and Jesse Davis. Quantifying the confidence of anomaly detectors in their example-wise predictions. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 227–243. Springer, 2020.

[RSL+24]

Amit Roy, Juan Shu, Jia Li, Carl Yang, Olivier Elshocht, Jeroen Smeets, and Pan Li. Gad-nr : graph anomaly detection via neighborhood reconstruction. In Proceedings of the 17th ACM International Conference on Web Search and Data Mining. 2024.

[WJD+21]

Xuhong Wang, Baihong Jin, Ying Du, Ping Cui, Yingshui Tan, and Yupu Yang. One-class graph neural networks for anomaly detection in attributed networks. Neural computing and applications, 33(18):12073–12085, 2021.

[XYFS07]

Xiaowei Xu, Nurcan Yuruk, Zhidan Feng, and Thomas AJ Schweiger. Scan: a structural clustering algorithm for networks. In Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, 824–833. 2007.

[XHZ+22]

Zhiming Xu, Xiao Huang, Yue Zhao, Yushun Dong, and Jundong Li. Contrastive attributed network anomaly detection with data augmentation. In Pacific-Asian Conference on Knowledge Discovery and Data Mining (PAKDD). 2022.

[YZY+21]

Xu Yuan, Na Zhou, Shuo Yu, Huafei Huang, Zhikui Chen, and Feng Xia. Higher-order structure based anomaly detection on attributed networks. In 2021 IEEE International Conference on Big Data (Big Data), 2691–2700. IEEE, 2021.