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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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. |