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..
Randomized Message-Interception Smoothing: Gray-box Certificates for Graph Neural Networks
Authors: Yan Scholten, Jan Schuchardt, Simon Geisler, Aleksandar Bojchevski, Stephan Günnemann
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our certificates on node classification datasets and analyze the robustness of existing GNN architectures. We demonstrate the effectiveness of our method on various models and datasets. |
| Researcher Affiliation | Academia | 1Dept. of Computer Science & Munich Data Science Institute, Technical University of Munich 2CISPA Helmholtz Center for Information Security |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | 1Project page: https://www.cs.cit.tum.de/daml/interception-smoothing |
| Open Datasets | Yes | We train our models on citation datasets: Cora-ML (Bojchevski and Günnemann, 2018; Mc Callum et al., 2000) with 2,810 nodes, 7,981 edges and 7 classes; Citeseer (Sen et al., 2008) with 2,110 nodes, 3,668 edges and 6 classes; and Pub Med (Namata et al., 2012) with 19,717 nodes, 44,324 edges and 3 classes. |
| Dataset Splits | Yes | As labelled nodes, we draw 20 nodes per class for training and validation, and 10% of the nodes for testing. |
| Hardware Specification | No | No specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments were found. |
| Software Dependencies | No | The paper mentions software like PyTorch Geometric and various GNN architectures (GCN, GAT, SMA) but does not provide specific version numbers for any of these software components. |
| Experiment Setup | Yes | As labelled nodes, we draw 20 nodes per class for training and validation, and 10% of the nodes for testing. We use n0 = 1,000 samples for estimating the majority class, n1 = 3,000 samples for certification, and set α = 0.01. |