Summary of Graphfm: a Comprehensive Benchmark For Graph Foundation Model, by Yuhao Xu et al.
GraphFM: A Comprehensive Benchmark for Graph Foundation Model
by Yuhao Xu, Xinqi Liu, Keyu Duan, Yi Fang, Yu-Neng Chuang, Daochen Zha, Qiaoyu Tan
First submitted to arxiv on: 12 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 proposed Graph Foundation Models rely on graph self-supervised learning, but outstanding issues persist regarding homogenization, scalability, efficiency, and training stop criteria. To address these questions, a rigorous benchmark is constructed to analyze the generalization and scalability of self-supervised Graph Neural Network (GNN) models. The performance of various GNN models is compared across tasks like node classification, link prediction, and node clustering, while scaling strategies are evaluated using full-batch and mini-batch methods. Additionally, training efficiency is assessed by measuring GPU memory usage and throughput. This study aims to provide insights for future research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Graph Foundation Models use graph self-supervised learning, but some problems need fixing. The study looks at how well these models do on different tasks, like classifying nodes or predicting links. It also tries to figure out how well they can handle big datasets and if there are better ways to train them. By doing this, the study hopes to help other researchers make progress. |
Keywords
» Artificial intelligence » Classification » Clustering » Generalization » Gnn » Graph neural network » Self supervised
