DAG Matters! GFlowNets Enhanced Explainer for Graph Neural Networks
Authors: Wenqian Li, Yinchuan Li, Zhigang Li, Jianye HAO, Yan Pang
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments on both synthetic and real datasets, and both qualitative and quantitative results show the superiority of our GFlow Explainer. |
| Researcher Affiliation | Collaboration | 1National University of Singapore, Singapore 2Huawei Noah s Ark Lab, Beijing, China 3Tianjin University, Tianjin, China |
| Pseudocode | Yes | We show the pseudocode of our GFlow Explainer for node classification and graph classification in Algorithm 1 and Algorithm 2 respectively. |
| Open Source Code | No | The paper lists links for baseline methods (e.g., GNNExplainer, PGExplainer, DEGREE, RGExplainer) but does not provide a link or explicit statement about the availability of its own code. |
| Open Datasets | Yes | We use six datasets, in which four synthetic datasets (BA-shapes,BA-Community,Tree Cycles and Tree-Grid) are used for the node classification task and two datasets (BA-2motifs and Mutagenicity) are used for the graph generation task. Details of these datasets are described in Appendix E.3 and the visualizations are shown in Figure 9. The BA-shapes data set consists of one Barabasi-Albert graph Barab asi & Albert (1999)...The Mutagenicity dataset is a real dataset... |
| Dataset Splits | No | Specifically, we vary the training set sized from {10%, 30%, 50%, 70%, 90%} and take the remaining instances for testing. The paper does not explicitly provide details for a validation split. |
| Hardware Specification | Yes | All experiments were conducted on a NVIDIA Quadro RTX 6000 environment with Pytorch. |
| Software Dependencies | No | All experiments were conducted on a NVIDIA Quadro RTX 6000 environment with Pytorch. Table 4 lists 'PyTorch' without a version number. |
| Experiment Setup | Yes | The parameters of GFlow Explainer are shown in Table 4. Table 4: Hyper-parameters: Batch Size 64, Number of layers of APPNP 3, α in APPNP 0.85, Hidden dimension 64, Architecture of MLP in L 64-8-1, Learning rate 1e-2, Optimizer Adam, Number of hops 3, Maximum size of generated sequences 20, Training epochs (node tasks) {50,100}, Training epochs (graph tasks) 100, Sample ratio of graph instance to train L 0.2 |