Summary of E-cgl: An Efficient Continual Graph Learner, by Jianhao Guo et al.
E-CGL: An Efficient Continual Graph Learner
by Jianhao Guo, Zixuan Ni, Yun Zhu, Siliang Tang
First submitted to arxiv on: 18 Aug 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 paper proposes an Efficient Continual Graph Learner (E-CGL) to tackle the challenges of continual graph learning, which involves learning from sequential graph data while preserving previous knowledge. The E-CGL addresses two main issues: interdependencies between different graph data and efficiency concerns when dealing with large graphs. To handle interdependencies, the paper demonstrates the effectiveness of replay strategies and introduces a combined sampling strategy that considers both node importance and diversity. For efficiency, E-CGL leverages a simple MLP model that shares weights with a GCN during training, circumventing the computationally expensive message passing process. The method outperforms nine baselines on four graph continual learning datasets under two settings, reducing catastrophic forgetting to an average of -1.1%. Additionally, E-CGL achieves 15.83x training time acceleration and 4.89x inference time acceleration across the four datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a new way to learn from graphs that keeps getting better as it goes along. It’s like a person who can remember what they learned before, but also learns new things without forgetting old ones. The method is called Efficient Continual Graph Learner (E-CGL), and it solves two big problems: how to deal with relationships between different graph data, and how to do this quickly when working with really big graphs. |
Keywords
» Artificial intelligence » Continual learning » Gcn » Inference
