Collective Robustness Certificates: Exploiting Interdependence in Graph Neural Networks
Authors: Jan Schuchardt, Aleksandar Bojchevski, Johannes Gasteiger, Stephan Günnemann
ICLR 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our approach on different datasets and with different base certificates. We show that incorporating locality alone is sufficient to obtain significantly better results. Our proposed certificate: ... 5 EXPERIMENTAL EVALUATION Experimental setup. We evaluate the proposed approach by certifiing node classifiers on multiple graphs and with different base certificates. |
| Researcher Affiliation | Academia | Jan Schuchardt, Aleksandar Bojchevski, Johannes Gasteiger & Stephan G unnemann Technical University of Munich, Germany {jan.schuchardt,bojchevs,j.gasteiger,guennemann}@in.tum.de |
| Pseudocode | Yes | Algorithm 1: Determining the pareto front of base certificates |
| Open Source Code | Yes | The code is publicly available under https://www.daml.in.tum.de/ collective-robustness/. We also uploaded the implementation as supplementary material. |
| Open Datasets | Yes | Datasets, models and base certificates. We train and certify models on the following datasets: Cora-ML (Mc Callum et al. (2000); Bojchevski & G unnemann (2018); N = 2810, 7981 edges, 7 classes), Citeseer (Sen et al. (2008); N = 2110, 3668 edges, 6 classes), Pub Med (Namata et al. (2012); N = 19717, 44324 edges, 3 classes), Reuters-21578 4 (N = 862, 2586 edges, 4 classes) and Web KB (Craven et al. (1998); N = 877, 2631 edges, 5 classes). |
| Dataset Splits | Yes | We use 20 nodes per class to construct a train and a validation set. We certify all remaining nodes. We repeat each experiment five times with different random initializations and data splits. |
| Hardware Specification | No | The paper mentions |
| Software Dependencies | No | The paper mentions specific components like |
| Experiment Setup | Yes | F HYPERPARAMETERS Training schedule for smoothed classifiers. Training is performed in a semi-supervised fashion with 20 nodes per class as a train set. Another 20 nodes per class serve as a validation set. Models are trained with Adam (learning rate = 0.001 [0.01 for SMA], β1 = 0.9, β2 = 0.999, ϵ = 10 8, weight decay = 0.001) for 3000 epochs, using the average cross-entropy loss across all training set nodes, with a batch size of 1. We employ early stopping, if the validation loss does not decrease for 50 epochs (300 epochs for SMA). In each epoch, a different graph is sampled from the smoothing distribution. |