Summary of Glemos: Benchmark For Instantaneous Graph Learning Model Selection, by Namyong Park et al.
GLEMOS: Benchmark for Instantaneous Graph Learning Model Selection
by Namyong Park, Ryan Rossi, Xing Wang, Antoine Simoulin, Nesreen Ahmed, Christos Faloutsos
First submitted to arxiv on: 2 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Social and Information Networks (cs.SI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents GLEMOS, a comprehensive benchmark environment for instantaneous graph learning (GL) model selection. The choice of GL model has a significant impact on downstream task performance, but selecting the right model is becoming increasingly difficult due to the growing number of models developed. To address this issue, the authors design GLEMOS, which provides extensive benchmark data for fundamental GL tasks such as link prediction and node classification. The benchmark includes performances of 366 models on 457 graphs and assesses representative model selection techniques in multiple evaluation settings. Additionally, GLEMOS is designed to be easily extended with new models, graphs, and performance records. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GLEMOS helps users quickly select an effective GL model without manual intervention. This is important because the number of GL models is increasing, making it hard to choose the right one. The authors created a big dataset with many models tested on different types of graphs. They also made sure that GLEMOS can be updated easily with new models or graphs. This tool will help researchers find better ways to pick GL models. |
Keywords
* Artificial intelligence * Classification
