Differentially Private Decoupled Graph Convolutions for Multigranular Topology Protection
Authors: Eli Chien, Wei-Ning Chen, Chao Pan, Pan Li, Ayfer Ozgur, Olgica Milenkovic
NeurIPS 2023 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To validate our approach, we conducted extensive experiments on seven node classification benchmarking and illustrative synthetic datasets. The results demonstrate that DPDGCs significantly outperform existing DP-GNNs in terms of privacy-utility trade-offs. |
| Researcher Affiliation | Academia | Eli Chien UIUC & Ga Tech EMAIL EMAIL Wei-Ning Chen Stanford University EMAIL Chao Pan UIUC EMAIL Pan Li Ga Tech EMAIL Ayfer Özgür Stanford University EMAIL Olgica Milenkovic UIUC EMAIL |
| Pseudocode | Yes | Appendix L: Pseudocode for GAP and DPDGC |
| Open Source Code | Yes | Our code is publicly available2. 2https://github.com/thupchnsky/dp-gnn |
| Open Datasets | Yes | We test 7 benchmark datasets available from either Pytorch Geometric library [42] or prior works. These datasets include the social network Facebook [43], citation networks Cora and Pubmed [44,45], Amazon co-purchase networks Photo and Computers [46], and Wikipedia networks Squirrel and Chameleon [47]. |
| Dataset Splits | No | The paper mentions using benchmark datasets and discusses training and testing, but it does not explicitly provide details about specific training, validation, and test dataset splits (e.g., percentages, sample counts, or references to predefined splits). |
| Hardware Specification | Yes | All experiments are performed on a Linux Machine with 48 cores, 376GB of RAM, and an NVIDIA Tesla P100 GPU with 12GB of GPU memory. |
| Software Dependencies | No | The paper mentions key software components such as 'Py Torch Geometric', 'autodp', and 'Opacus' but does not provide specific version numbers for these dependencies. |
| Experiment Setup | Yes | For all methods, we set the hidden dimension to 64, and use Se LU [54] as the nonlinear activation function. The learning rate is set to 10 3, and do not decay the weights. Training involves 100 epochs for both pretraining and classifier modules. We use a dropout 0.5 for nonprivate and edge GDP experiments and no dropout for the node GDP and k-neighbor GDP experiments. |