Randomized Message-Interception Smoothing: Gray-box Certificates for Graph Neural Networks
Authors: Yan Scholten, Jan Schuchardt, Simon Geisler, Aleksandar Bojchevski, Stephan Günnemann
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | 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. |