Differentially Private Densest Subgraph Detection
Authors: Dung Nguyen, Anil Vullikanti
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our algorithms on a large number of real-world networks, and observe a good privacy-accuracy tradeoff when the network has high density. |
| Researcher Affiliation | Academia | 1Department of Computer Science, University of Virginia, Virginia, USA 2Biocomplexity Institute and Inintiative, University of Virginia, Virginia, USA. |
| Pseudocode | Yes | Algorithm 1 SEQDENSEDP(G, ϵ, δ), Algorithm 2 PARDENSEDP(G, ϵ, δ), Algorithm 3 PHASEDENSEDP(G, ϵ, δ) |
| Open Source Code | No | The paper does not provide a direct link to open-source code for the described methodology or explicitly state that the code is publicly available. |
| Open Datasets | Yes | Table 1 lists 6 different networks from SNAP database we use to evaluate our results these are chosen to be of various sizes, in order to understand the impact of network structure on the results (Leskovec & Krevl, 2014). |
| Dataset Splits | No | The paper does not provide specific details on training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not mention any specific hardware specifications (e.g., GPU, CPU models, or memory) used for running the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers needed for replication. |
| Experiment Setup | No | The paper does not provide specific experimental setup details such as hyperparameter values, optimizer settings, or other training configurations. |