ProtGNN: Towards Self-Explaining Graph Neural Networks
Authors: Zaixi Zhang, Qi Liu, Hao Wang, Chengqiang Lu, Cheekong Lee9127-9135
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we evaluate our method on a wide range of datasets and perform concrete case studies. Extensive results show that Prot GNN and Prot GNN+ can provide inherent interpretability while achieving accuracy on par with the noninterpretable counterparts. |
| Researcher Affiliation | Collaboration | 1 Anhui Province Key Lab. of Big Data Analysis and Application, School of Computer Science and Technology, University of Science and Technology of China 2 Tencent America |
| Pseudocode | Yes | Algorithm 1: Overview of Prot GNN/Prot GNN+ Training |
| Open Source Code | Yes | The implementation is publicly available at https://github.com/zaixizhang/Prot GNN. |
| Open Datasets | Yes | MUTAG (Debnath et al. 1991) and BBBP (Wu et al. 2018) are molecule datasets for graph classification...Graph-SST2 (Socher et al. 2013) and Graph-Twitter (Dong et al. 2014) are sentiment graph datasets...BA-Shape is a synthetic node classification dataset. |
| Dataset Splits | Yes | The split for train/validation/test sets is 80% : 10% : 10%. |
| Hardware Specification | Yes | All our experiments are conducted with one Tesla V100 GPU. |
| Software Dependencies | No | The paper mentions using the ADAM optimizer, GCN, GAT, GIN, and Bert word embeddings but does not provide specific version numbers for any software libraries or frameworks (e.g., PyTorch, TensorFlow, scikit-learn). |
| Experiment Setup | Yes | All models are trained for 500 epochs with an early stopping strategy based on accuracy on the validation set. We adopt the ADAM optimizer with a learning rate of 0.005. In Eq.(3), the hyperparameters λ1, λ2, and λ3 are set to 0.10, 0.05, and 0.01 respectively. smax is set to 0.3 in Eq. (6). The number of prototypes per class m is set to 5. In MCTS for prototype projection, we set λ in Eq. (9) to 5 and the number of iterations to 20. Each node in the Monte Carlo Tree can expand up to 10 child nodes and Nmin is set to 5. The prototype projection period τ is set to 50 and the projection epoch Tp is set to 100. In the training of Prot GNN+, the warm-up epoch Tw is set to 200. We employ a three-layer neural network to learn edge weights. In Eq. (14), λb is set to 0.01 and B is set to 10. |