Poincaré Differential Privacy for Hierarchy-Aware Graph Embedding
Authors: Yuecen Wei, Haonan Yuan, Xingcheng Fu, Qingyun Sun, Hao Peng, Xianxian Li, Chunming Hu
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiment results on five real-world datasets demonstrate the proposed Poin DP s advantages of effective privacy protection while maintaining good performance on the node classification task. |
| Researcher Affiliation | Academia | Yuecen Wei1,2,3, Haonan Yuan1, Xingcheng Fu3*, Qingyun Sun1, Hao Peng1, Xianxian Li3, Chunming Hu1,2* 1Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, Beijing, China 2School of Software, Beihang University, Beijing, China 3Key Lab of Education Blockchain and Intelligent Technology, Ministry of Education, Guangxi Normal University, China |
| Pseudocode | Yes | Algorithm 1: Overall training process of Poin DP |
| Open Source Code | Yes | Code is available at https://github.com/WYLucency/Poin DP. |
| Open Datasets | Yes | For datasets (see Appendix B.1), we chose three citation networks (Cora, Citeseer and Pub Med) and two E-commerce networks in Amazon (Computers and Photo). |
| Dataset Splits | Yes | Our dataset split follows the Py Torch Geometric. |
| Hardware Specification | Yes | We conducted the experiments with NVIDIA Ge Force RTX 3090 with 16GB of Memory. |
| Software Dependencies | No | The paper mentions 'Py Torch Geometric' but does not provide specific version numbers for this or any other software dependencies. |
| Experiment Setup | Yes | The learning rate lr is 0.005, the privacy budget ϵ to be [0, 1], and the training iterations E to be 200. |