ADGCN: A Weakly Supervised Framework for Anomaly Detection in Social Networks

Author:

Shen ZhixiangORCID,Zhang TianleORCID,He Haolan

Publisher

Springer Nature Singapore

Reference24 articles.

1. Bo, D., Wang, X., Shi, C., Shen, H.: Beyond low-frequency information in graph convolutional networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 3950–3957 (2021)

2. Cai, S., et al.: Rethinking graph neural architecture search from message-passing. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6657–6666 (2021)

3. Clevert, D.A., Unterthiner, T., Hochreiter, S.: Fast and accurate deep network learning by exponential linear units (ELUS). arXiv preprint arXiv:1511.07289 (2015)

4. Gilmer, J., Schoenholz, S.S., Riley, P.F., Vinyals, O., Dahl, G.E.: Neural message passing for quantum chemistry. In: International Conference on Machine Learning, pp. 1263–1272. PMLR (2017)

5. Hamilton, W., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. In: Advances in Neural Information Processing Systems 30 (2017)

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