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..
Robustness of Graph Neural Networks at Scale
Authors: Simon Geisler, Tobias Schmidt, Hakan Şirin, Daniel Zügner, Aleksandar Bojchevski, Stephan Günnemann
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our attacks and defense with standard GNNs on graphs more than 100 times larger compared to previous work. |
| Researcher Affiliation | Academia | Department of Informatics Technical University of Munich EMAIL |
| Pseudocode | Yes | Algorithm 1 Projected Randomized Block Coordinate Descent (PR-BCD) |
| Open Source Code | Yes | For supplementary material including the code and configuration see https://www.in.tum.de/daml/robustness-of-gnns-at-scale. |
| Open Datasets | Yes | Cora ML [2] 2.8 k 35.88 MB 168.32 k B Citeseer [28] 3.3 k 43.88 MB 94.30 k B Pub Med [33] 19.7 k 1.56 GB 1.77 MB ar Xiv [21] 169.3 k 114.71 GB 23.32 MB Products [21] 2.4 M 23.99 TB 2.47 GB Papers 100M [21] 111.1 M 49.34 PB 32.31 GB |
| Dataset Splits | Yes | For the OGB datasets we use the public splits and otherwise sample 20 nodes per class for training/validation. We typically report the average over three random seeds/splits and the 3-sigma error of the mean. |
| Hardware Specification | Yes | We only use a 32GB Tesla V100 for the experiments on Products with a full-batch GNN, since a three-layer GCN requires roughly 30 GB already during training. |
| Software Dependencies | No | The paper does not explicitly list specific software dependencies with version numbers. |
| Experiment Setup | Yes | On ar Xiv (170 k nodes), we train for 500 epochs and run the global PR-BCD attack for 500 epochs. The whole training and attacking procedure requires less than 2 minutes. |