Differentially Private Graph Diffusion with Applications in Personalized PageRanks
Authors: Rongzhe Wei, Eli Chien, Pan Li
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on real-world network data demonstrate the superiority of our method under stringent privacy conditions. In this section, we present empirical evaluations to support our theoretical findings. Specifically, we apply the widely-used PPR algorithm (Sec. 3.4) to real-world graphs. |
| Researcher Affiliation | Academia | Rongzhe Wei Georgia Institute of Technology rongzhe.wei@gatech.edu Eli Chien Georgia Institute of Technology ichien6@gatech.edu Pan Li Georgia Institute of Technology panli@gatech.edu |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks with explicit labels like 'Algorithm' or 'Pseudocode'. |
| Open Source Code | Yes | We have uploaded our code in supplementary file. We have uploaded our code in the supplementary file with instructions (README.md) to reproduce the results. |
| Open Datasets | Yes | We conduct experiments on the following datasets: Blog Catalog [57], a social network of bloggers with 10,312 nodes and 333,983 edges; Flickr [57], a photo-sharing social network with 80,513 nodes and 5,899,882 edges; and The Marker [58], an online social network with 69,400 nodes and 1,600,000 edges. |
| Dataset Splits | No | The paper does not provide specific training, validation, or test dataset split information (percentages, counts, or specific predefined splits). |
| Hardware Specification | Yes | Experiments were performed on a server with two AMD EPYC 7763 64-Core Processors, 2TB DRAM, six NVIDIA RTX A6000 GPUs (each with 48GB of memory). |
| Software Dependencies | No | The paper does not explicitly list software dependencies with specific version numbers in its main text or appendices. |
| Experiment Setup | Yes | PPR with parameter β = 0.8. Considering that both our approach and DP-PUSHFLOWCAP employ a thresholding parameter η to balance the privacy-utility trade-off, we select η from a set of seven values spanning orders of magnitude from 10-10 to 10-4... For each experiment, we randomly choose an initial seed for diffusion and execute PPR for 100 iterations following [20]. |