Verifying message-passing neural networks via topology-based bounds tightening
Authors: Christopher Hojny, Shiqiang Zhang, Juan S Campos, Ruth Misener
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To demonstrate the effectiveness of these strategies, we implement an extension to the open-source branch-andcut solver SCIP. We test on both node and graph classification problems and consider topological attacks that both add and remove edges. |
| Researcher Affiliation | Academia | 1Eindhoven University of Technology, Eindhoven, The Netherlands 2Department of Computing, Imperial College London, UK. |
| Pseudocode | Yes | Algorithm 1 Static bounds tightening (sbt) ... Algorithm 2 Aggressive bounds tightening (abt) |
| Open Source Code | Yes | The code is available at Git Hub, also see Hojny & Zhang (2024). Hojny, C. and Zhang, S. SCIP-MPNN: Code for the paper Verifying message-passing neural networks via topologybased bounds tightening . https://doi.org/10. 5281/zenodo.11208355, 2024. |
| Open Datasets | Yes | We evaluate the performance of various verification methods on benchmarks including: (i) MUTAG and ENZYMES (Morris et al., 2020) for graph classification, and (ii) Cora and Cite Seer (Yang et al., 2023) for node classification. All datasets are available in Py G and summarized in Table 1. |
| Dataset Splits | No | For graph classification, ... 30% of the graphs are used to train the model. For node classification, ... 10% of the nodes are used for training. This only specifies the training split, without providing explicit percentages or counts for validation and test splits. |
| Hardware Specification | Yes | All experiments have been conducted on a Linux cluster with 12 Intel Xeon Platinum 8260 2.40 GHz processors each having 48 physical threads. |
| Software Dependencies | Yes | All GNNs are built and trained using Py G (Py Torch Geometric) 2.1.0 (Fey & Lenssen, 2019). All MIPs are implemented in C/C++ using the open-source MIP solver SCIP 8.0.4 (Bestuzheva et al., 2023); all LP relaxations are solved using Soplex 6.0.4 (Gamrath et al., 2020). ...Gurobi 10.0.3 (GRBbasic, GRBsbt) (Gurobi Optimization, LLC, 2023). |
| Experiment Setup | Yes | All models are trained 200 epochs with learning rate 0.01, weight decay 10 4, and dropout 0.5. ... In our experiments, we choose s {2, 3, 4}, and use δ percentage of the number of edges as the global budget Q, where 1 δ 10. For node classification, ... We set 10 as the global budget and 5 as the local budget. |