On Explainability of Graph Neural Networks via Subgraph Explorations
Authors: Hao Yuan, Haiyang Yu, Jie Wang, Kang Li, Shuiwang Ji
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that our Subgraph X achieves significantly improved explanations, while keeping computations at a reasonable level. |
| Researcher Affiliation | Academia | 1Department of Computer Science & Engineering, Texas A&M University, TX, USA 2Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, China 3West China Biomedical Big Data Center, West China Hospital, Chengdu, China. |
| Pseudocode | Yes | Finally, we conclude the computation steps of our proposed Subgraph X in Algorithm 1 and 2. |
| Open Source Code | Yes | Our code and data are now publicly available in the DIG library (Liu et al., 2021)1. 1https://github.com/divelab/DIG |
| Open Datasets | Yes | MUTAG (Debnath et al., 1991) and BBBP (Wu et al., 2018) are molecular datasets for graph classification tasks. ...Graph-SST2 (Yuan et al., 2020c) is sentiment graph dataset... |
| Dataset Splits | No | The paper describes the datasets used (MUTAG, BBBP, GRAPH-SST2, BA-2MOTIFS, BA-SHAPE) and general experimental settings, but does not explicitly provide specific training, validation, or test split percentages or sample counts in the main text. |
| Hardware Specification | Yes | We conduct our experiments using one Nvidia V100 GPU on an Intel Xeon Gold 6248 CPU. |
| Software Dependencies | Yes | Our implementations are based on Python 3.7.6, Py Torch 1.6.0, and Torch-geometric 1.6.3. |
| Experiment Setup | Yes | For our proposed Subgraph X and other algorithms with MCTS, the MCTS iteration number M is set to 20. To explore a suitable trade-off between exploration and exploitation, we set the hyperparameter λ in Eq.(3) to 5 for Graph-SST2 (GATs) and BBBP (GCNs) models, and 10 for other models. Since all GNN models contain 3 network layers, we consider 3-hop computational graphs to compute Shapley values for our Subgraph X. For the Monte-Carlo sampling in our Subgraph X, we set the Monte-Carlo sampling steps T to 100 for all datasets. |