Summary of Graph Continual Learning with Debiased Lossless Memory Replay, by Chaoxi Niu et al.
Graph Continual Learning with Debiased Lossless Memory Replay
by Chaoxi Niu, Guansong Pang, Ling Chen
First submitted to arxiv on: 17 Apr 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 research paper introduces a novel framework for graph continual learning (GCL), called Debiased Lossless Memory replay (DeLoMe). The framework aims to adapt graph neural networks (GNNs) to continually updated graph data while maintaining performance on previous tasks. Unlike existing methods, DeLoMe learns small lossless synthetic node representations as memory, which preserves graph data privacy and captures holistic graph information. A debiased GCL loss function is devised to alleviate bias toward the current task due to data imbalance between classes in the memory data and current data. The paper demonstrates the effectiveness of DeLoMe through extensive experiments on four graph datasets under both class- and task-incremental learning settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us learn better from expanding graphs. When we want computers to understand graphs, they need to be updated constantly. This is called graph continual learning (GCL). Some methods try to remember what was learned before by replaying old data. But this can cause problems if the new data is different. The researchers created a new method called DeLoMe that solves these issues. They use small representations of nodes in the graph, which keeps the data private and helps them learn better from all the information. This works well on many types of graphs. |
Keywords
» Artificial intelligence » Continual learning » Loss function
