Hierarchical Multi-Marginal Optimal Transport for Network Alignment
Authors: Zhichen Zeng, Boxin Du, Si Zhang, Yinglong Xia, Zhining Liu, Hanghang Tong
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments and analysis show that our proposed HOT achieves significant improvements over the state-of-the-art in both effectiveness and scalability. |
| Researcher Affiliation | Collaboration | Zhichen Zeng1, Boxin Du2, Si Zhang3, Yinglong Xia3, Zhining Liu1, Hanghang Tong1 1University of Illinois Urbana-Champaign 2Amazon 3Meta |
| Pseudocode | No | The paper has an 'Optimization Algorithm' section detailing iterative calculations (e.g., Eq. 8-13), but it does not present these as a formal 'Pseudocode' or 'Algorithm' block/figure. |
| Open Source Code | Yes | 1Code and datasets are available at https://github.com/ zhichenz98/HOT-AAAI24 |
| Open Datasets | Yes | 1Code and datasets are available at https://github.com/ zhichenz98/HOT-AAAI24 |
| Dataset Splits | Yes | To mitigate the effect of data split, we randomly split the datatsets into 10 folds, using 1 fold (i.e., 10%) for training and the rest 9 folds for testing. We report the mean and standard deviation of the alignment results with different training/test splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running the experiments. It only generally refers to 'GPU' in a related work context without linking it to their own experimental setup. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | In our experiments, we adopt a consistent parameter setting with λ=10 3, α=0.5, and β =0.15. For number of clusters, we set M = n 50 for all datasets. |