IFGNN: An Individual Fairness Awareness Model for Missing Sensitive Information Graphs

Author:

Xu Kejia,Fei Zeming,Yu Jianke,Kong Yu,Wang Xiaoyang,Zhang Wenjie

Publisher

Springer Nature Switzerland

Reference43 articles.

1. Agarwal, C., Lakkaraju, H., Zitnik, M.: Towards a unified framework for fair and stable graph representation learning. In: Uncertainty in Artificial Intelligence, pp. 2114–2124. PMLR (2021)

2. Awasthi, A., Garov, A.K., Sharma, M., Sinha, M.: GNN model based on node classification forecasting in social network. In: AISC, pp. 1039–1043 (2023)

3. Beutel, A., Chen, J., Zhao, Z., Chi, E.H.: Data decisions and theoretical implications when adversarially learning fair representations. arXiv preprint arXiv:1707.00075 (2017)

4. Chen, X., Chen, S., Yao, J., Zheng, H., Zhang, Y., Tsang, I.W.: Learning on attribute-missing graphs. IEEE Trans. Pattern Anal. Mach. Intell. 44(2), 740–757 (2020)

5. Cheng, D., Chen, C., Wang, X., Xiang, S.: Efficient top-k vulnerable nodes detection in uncertain graphs. IEEE Trans. Knowl. Data Eng. (2021)

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