Robustness of Graph Neural Networks at Scale
Authors: Simon Geisler, Tobias Schmidt, Hakan Şirin, Daniel Zügner, Aleksandar Bojchevski, Stephan Günnemann
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | 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 {geisler, schmidtt, sirin, zuegnerd, bojchevs, guennemann}@in.tum.de |
| 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. |