Summary of Continual Learning on Graphs: a Survey, by Zonggui Tian et al.
Continual Learning on Graphs: A Survey
by Zonggui Tian, Du Zhang, Hong-Ning Dai
First submitted to arxiv on: 9 Feb 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 This paper provides a comprehensive survey on recent efforts in continual graph learning, a technique increasingly adopted for diverse graph-structured data processing tasks in non-stationary environments. The authors focus on mitigating catastrophic forgetting while improving performance continuously, rather than just addressing forgetting. They introduce a new taxonomy of continual graph learning methods from the perspective of overcoming forgetting and analyze challenges and possible solutions for continuous performance improvement. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how computers learn from graphs that change over time. Right now, there are ways to stop this “forgetting” problem, but not many ways to actually make the computer get better as it learns. This article shows what’s been tried so far and what still needs to be done to make continual graph learning work better. It also talks about how this can help us learn more from data that is constantly changing. |
