Summary of Exploring Correlations Of Self-supervised Tasks For Graphs, by Taoran Fang et al.
Exploring Correlations of Self-Supervised Tasks for Graphs
by Taoran Fang, Wei Zhou, Yifei Sun, Kaiqiao Han, Lvbin Ma, Yang Yang
First submitted to arxiv on: 7 May 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 This paper explores graph self-supervised learning, which enables the training of informative representations without labeled data. The authors evaluate the performance of representations trained on one task on other tasks and define correlation values to quantify these relationships. By analyzing these correlations across various datasets, they reveal the complexity of task relationships and limitations of existing multi-task learning methods. To address this, the authors propose Graph Task Correlation Modeling (GraphTCM) to illustrate task correlations and enhance graph self-supervised training. The results show that their method outperforms existing methods on various downstream tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how we can learn things about graphs without needing labels. Researchers have been trying to figure this out, but they haven’t fully understood what’s going on. This paper tries to explain it better by looking at how different ways of learning relate to each other. They found that these relationships are really complicated and that some methods don’t work as well as others. To fix this, the researchers came up with a new way called Graph Task Correlation Modeling (GraphTCM) that helps us learn more about graphs without needing labels. The results show that their method does better than other methods on different tasks. |
Keywords
» Artificial intelligence » Multi task » Self supervised
