DGExplainer: Explaining Dynamic Graph Neural Networks via Relevance Back-propagation
Authors: Yezi Liu, Jiaxuan Xie, Yanning Shen
IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Quantitative and qualitative experiments on six real-world datasets demonstrate that DGExplainer effectively identiļ¬es 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. |