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..
A Differential Geometric View and Explainability of GNN on Evolving Graphs
Authors: Yazheng Liu, Xi Zhang, Sihong Xie
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on node classification, link prediction, and graph classification tasks with evolving graphs demonstrate the better sparsity, faithfulness, and intuitiveness of the proposed method over the state-of-the-art methods. |
| Researcher Affiliation | Academia | Key Laboratory of Trustworthy Distributed Computing and Service (Mo E), BUPT, Beijing, China Department of Computer Science and Engineering, Lehigh University, Bethlehem, PA, USA EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Compute Cp,j for a target node J. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code or a direct link to a code repository for the described methodology. |
| Open Datasets | Yes | We study node classification task on evolving graphs on the Yelp Chi, Yelp NYC, Yelp Zip Rayana & Akoglu (2015), Pheme Zubiaga et al. (2017) and Weibo Ma et al. (2018) datasets, and study the link prediction task on the BC-OTC, BC-Alpha, and UCI datasets. These datasets have time stamps and the graph evolutions can be identified. The molecular data (MUTAG Debnath et al. (1991) is used for the graph classification. |
| Dataset Splits | No | The paper mentions using a "training set" but does not specify explicit train/validation/test dataset splits (e.g., percentages, absolute sample counts, or citations to predefined splits) to reproduce the experiment. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU/CPU models, memory, cloud instance types) used to run its experiments. |
| Software Dependencies | No | The paper mentions using the "cvxpy library Diamond & Boyd" but does not specify a version number for cvxpy or any other key software components used in the experiments. |
| Experiment Setup | Yes | We set the learning rate to 0.01, the dropout to 0.2 and the hidden size to 16 when we train the GNN model. |