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].
Gromov-Wasserstein Learning for Graph Matching and Node Embedding
Authors: Hongteng Xu, Dixin Luo, Hongyuan Zha, Lawrence Carin Duke
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We apply the Gromov-Wasserstein learning (GWL) method to both synthetic and real-world matching tasks, and compare it with state-of-the-art methods. In our experiments, we set hyperparameters as follows: the number of outer iterations is M = 30, the number of inner iteration is N = 200, γ = 0.01 and L( , ) is the MSE loss. |
| Researcher Affiliation | Collaboration | 1Infinia ML, Inc., Durham, NC, USA 2Department of ECE, Duke University, Durham, NC, USA 3College of Computing, Georgia Institute of Technology, Atlanta, GA, USA. |
| Pseudocode | Yes | Algorithm 1 Gromov-Wasserstein Learning (GWL) |
| Open Source Code | Yes | The code is available on https://github.com/Hongteng Xu/gwl. |
| Open Datasets | Yes | MC3 is a dataset used in the Mini-Challenge 3 of VAST Challenge 2018, which records the communication behavior among a company s employees on different networks.1 The communications are categorized into two types: phone calls and emails between employees. ... 1http://vacommunity.org/VAST+Challenge+2018+MC3 |
| Dataset Splits | Yes | For all the methods, we use 50% of the admissions for training, 25% for validation, and the remaining 25% for testing. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU types, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper mentions using Adam as an optimizer, but does not provide specific version numbers for any software dependencies or libraries (e.g., Python, deep learning frameworks, or numerical libraries) used in the experiments. |
| Experiment Setup | Yes | In our experiments, we set hyperparameters as follows: the number of outer iterations is M = 30, the number of inner iteration is N = 200, γ = 0.01 and L( , ) is the MSE loss. We tried βs in {0, 1, 10, 100, 1000} and the β in [1, 100] achieves stable performance. Therefore, we empirically set β = 10. When solving (8), we use Adam (Kingma & Ba, 2014) with learning rate 0.001 and set the number of epochs to 5, and the size of batches as 100. |