Summary of Towards Robust Graph Incremental Learning on Evolving Graphs, by Junwei Su et al.
Towards Robust Graph Incremental Learning on Evolving Graphs
by Junwei Su, Difan Zou, Zijun Zhang, Chuan Wu
First submitted to arxiv on: 20 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 tackles the challenge of incremental learning on graph-structured data, where models must learn from a stream of tasks without forgetting previously learned information. Specifically, it focuses on Node-wise Graph Incremental Learning (NGIL), which involves predicting tasks for individual nodes in a graph. The authors formalize the problem and propose a novel regularization-based technique called Structural-Shift-Risk-Mitigation (SSRM) to mitigate the impact of structural shifts induced by emerging tasks. SSRM is shown to improve the performance of state-of-the-art GNN incremental learning frameworks in the inductive setting. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps machines learn from lots of small tasks, like predicting what people will do on a social media platform. It’s hard because each task might be different, and the machine might forget old things it learned. The researchers found that when new tasks come along, they can make the old tasks harder to predict too. They invented a way called Structural-Shift-Risk-Mitigation (SSRM) to help machines remember old things even better. It makes existing models work better on new tasks. |
Keywords
* Artificial intelligence * Gnn * Regularization
