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..
Line Graph Vietoris-Rips Persistence Diagram for Topological Graph Representation Learning
Authors: Jaesun Shin, Eunjoo Jeon, Taewon Cho, Namkyeong Cho, Youngjune Gwon
JMLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally we empirically validate superior performance of our models on several graph classification and regression benchmarks. Keywords: Graph Neural Network, Persistence Diagram, Topological Data Analysis, Weisfeiler-Lehman Test, Vietoris-Rips Filtration |
| Researcher Affiliation | Collaboration | Jaesun Shin EMAIL Samsung SDS; Namkyeong Cho1 EMAIL Center for Mathematical Machine Learning and its Applications(CM2LA), Department of Mathematics POSTECH |
| Pseudocode | Yes | Pseudocode for the construction of LGVR is described in Algorithm 1 (See Figure 4 for the overall framework). |
| Open Source Code | Yes | Our code is publicly available at https://github.com/samsungsds-research-papers/LGVR. |
| Open Datasets | Yes | For classification, we test our method on 7 benchmark graph datasets: 5 bioinformatics datasets (MUTAG, PTC, PROTEINS, NCI1, NCI109) that represent chemical compounds or protein substructures, and two social network datasets (IMDB-B, IMDB-M) (Yanardag and Vishwanathan (2015)). For the regression task, we experiment on a standard graph benchmark QM9 dataset (Ramakrishnan et al. (2014); Ruddigkeit et al. (2012); Wu et al. (2018)). |
| Dataset Splits | Yes | Since there is no separate test set for these datasets, for a fair comparison, we follow the standard 10-fold cross-validation based on the same split used by Zhang et al. (2018) and report the results according to the evaluation protocol described by Maron et al. (2019): ... The dataset is split into 80% train, 10% validation, and 10% test. |
| Hardware Specification | Yes | We run all experiments on a single DGX-A100 GPU. |
| Software Dependencies | No | The paper mentions using the pytorch-geometric library and Adam optimizer but does not specify version numbers for these software components. For example, in Section D.1.3, it states: "Each graph is represented by an adjacency matrix and input node features, which can be obtained from the pytorch-geometric library Fey and Lenssen (2019)." However, no version for pytorch-geometric is provided. |
| Experiment Setup | Yes | All hyperparameters were carried out on the same set based on C (Appendix D.2): the learning rate is set to {5e-3, 1e-3, 5e-4, 1e-4, 5e-5} for GIN while it is set to {1e-4, 5e-5} for PPGN. The decay rate is set to {0.5, 0.75, 1.0} with Adam optimizer (Kingma and Ba (2014)), and we implement all the models by tuning hyperparameters based on the validation score. Tables 7 and 8 in Appendix D.2 provide specific LR, DR, BS, Ep for each dataset. |