Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Certifiable Robustness to Graph Perturbations
Authors: Aleksandar Bojchevski, Stephan Günnemann
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate our claims on two publicly available datasets: Cora-ML (N = 2, 995, |E| = 8, 416, D = 2, 879, K = 7) [4, 30] and Citeseer (N = 3, 312, |E| = 4, 715, D = 3, 703, K = 6) [37] with further experiments on Pubmed (N = 19, 717, |E| = 44, 324, D = 500, K = 3) [37] in the appendix. |
| Researcher Affiliation | Academia | Aleksandar Bojchevski Technical University of Munich EMAIL Stephan Günnemann Technical University of Munich EMAIL |
| Pseudocode | Yes | Algorithm 1 POLICY ITERATION WITH LOCAL BUDGET |
| Open Source Code | Yes | the code is provided for reproducibility1. Footnote 1: Code, data, and supplementary material available at https://www.daml.in.tum.de/graph-cert |
| Open Datasets | Yes | We demonstrate our claims on two publicly available datasets: Cora-ML (N = 2, 995, |E| = 8, 416, D = 2, 879, K = 7) [4, 30] and Citeseer (N = 3, 312, |E| = 4, 715, D = 3, 703, K = 6) [37] with further experiments on Pubmed (N = 19, 717, |E| = 44, 324, D = 500, K = 3) [37] in the appendix. |
| Dataset Splits | Yes | We select 20 nodes per class for the train/validation set and use the rest for the test set. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory amounts) were found. The paper does not mention any specific hardware used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies, libraries, or solvers used in the experiments. |
| Experiment Setup | Yes | We configure π-PPNP with one hidden layer of size 64 and set α = 0.85. |