Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
DAG Matters! GFlowNets Enhanced Explainer for Graph Neural Networks
Authors: Wenqian Li, Yinchuan Li, Zhigang Li, Jianye HAO, Yan Pang
ICLR 2023 | Venue PDF | 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 |