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..
DGExplainer: Explaining Dynamic Graph Neural Networks via Relevance Back-propagation
Authors: Yezi Liu, Jiaxuan Xie, Yanning Shen
IJCAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Quantitative and qualitative experiments on six real-world datasets demonstrate that DGExplainer effectively identi๏ฌes critical nodes for link prediction and node regression tasks in dynamic GNNs. |
| Researcher Affiliation | Academia | University of California, Irvine EMAIL, EMAIL |
| Pseudocode | No | No explicit pseudocode or algorithm block (e.g., "Algorithm 1") is present in the provided text, although it is referenced. |
| Open Source Code | Yes | Appendix available at https://github.com/yezil3/DGExplainer IJCAI/blob/main/IJCAI appendix.pdf |
| Open Datasets | Yes | Datasets. We evaluate the proposed framework on six realworld datasets. For the link prediction tasks, we use four datasets: Reddit Hyperlink (Reddit) [Kumar et al., 2018], Enron [Klimt and Yang, 2004], Facebook (FB) [Trivedi et al., 2019], and COLAB [Rahman and Al Hasan, 2016]. For the node regression tasks, we use two datasets: Pe MS04 and Pe MS08 [Guo et al., 2019]1. The statistics of these datasets and the initial performance of GCN-GRU on them are presented in Appendix A.2. 1pems.dot.ca.gov |
| Dataset Splits | No | The paper mentions using datasets and evaluating performance but does not explicitly provide specific training/test/validation dataset splits (e.g., percentages or counts) within the provided text. It refers to 'experimental setup of a previous work [Pareja et al., 2020]' for evaluation but not for data partitioning. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU models, CPU types, memory details) used to run its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies, such as library names with version numbers, used to replicate the experiment. |
| Experiment Setup | No | The paper states that 'Implementation details are provided in Appendix A.4' but does not include specific hyperparameter values or detailed training configurations within the main text. |