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 [1].
A Variational Edge Partition Model for Supervised Graph Representation Learning
Authors: Yilin He, Chaojie Wang, Hao Zhang, Bo Chen, Mingyuan Zhou
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive evaluations on real-world graph datasets have verified the effectiveness of the proposed method in learning discriminative representations for both node-level and graph-level classification tasks. |
| Researcher Affiliation | Academia | 1The University of Texas at Austin, 2Nanyang Technological University, 3Xidian University |
| Pseudocode | No | The paper describes the model components and training process, but it does not contain any structured pseudocode or algorithm blocks that are clearly labeled as 'Pseudocode' or 'Algorithm'. |
| Open Source Code | Yes | Code available at https://github.com/YH-Ut MSB/VEPM |
| Open Datasets | Yes | For node classification, we consider three citation networks (Cora, Citeseer, and Pubmed) and a Wikipedia-based online article network (Wiki CS) [32]... For graph classification, we consider four bioinformatics datasets (MUTAG, PTC, NCI1, PROTEINS) and four social network datasets (IMDBBINARY, IMDB-MULTI, REDDIT-BINARY, REDDIT-MULTI). |
| Dataset Splits | Yes | Most of the baselines are compared following the 10-fold crossvalidation-based evaluation protocol proposed by Xu et al. [6]. For the graph classification task, we also evaluate our model following Zhang and Chen [33], which conducts a more rigorous trainvalidation-test protocol. |
| Hardware Specification | No | The paper mentions 'the Texas Advanced Computing Center (TACC) for providing HPC resources', but it does not specify any particular hardware details such as GPU models, CPU models, or memory. |
| Software Dependencies | No | The paper does not provide specific software dependencies, such as library names with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | No | The paper mentions 'More details about the experiments are elaborated in Appendix C.2.' but this appendix is not provided in the given text. The main body of the paper discusses datasets and evaluation protocols but does not specify concrete hyperparameter values or training configurations. |