Summary of Decoupling Weighing and Selecting For Integrating Multiple Graph Pre-training Tasks, by Tianyu Fan et al.
Decoupling Weighing and Selecting for Integrating Multiple Graph Pre-training Tasks
by Tianyu Fan, Lirong Wu, Yufei Huang, Haitao Lin, Cheng Tan, Zhangyang Gao, Stan Z. Li
First submitted to arxiv on: 3 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 Weigh And Select (WAS) framework integrates multiple graph pre-training tasks by combining two collaborative processes: selecting an optimal task combination and weighing the importance of each task. WAS uses decoupled siamese networks to learn a customized instance-level task weighting strategy, based on adapting to different instances in a given task pool. This approach is evaluated on 16 graph datasets across node-level and graph-level downstream tasks, demonstrating comparable performance to leading counterparts when combining simple yet classical tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows how to combine different graph pre-training tasks to get even better results. It proposes a new way to do this called Weigh And Select (WAS). WAS works by choosing the best combination of tasks for each specific instance and then weighing their importance. The authors tested WAS on many different datasets and found that it can achieve similar performance to other top methods, but using fewer complex tasks. |
