A Differential Geometric View and Explainability of GNN on Evolving Graphs
Authors: Yazheng Liu, Xi Zhang, Sihong Xie
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | 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 {liuyz,zhangx}@bupt.edu.cn, xiesihong1@gmail.com |
| 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. |