Summary of Incremental Learning with Concept Drift Detection and Prototype-based Embeddings For Graph Stream Classification, by Kleanthis Malialis and Jin Li and Christos G. Panayiotou and Marios M. Polycarpou
Incremental Learning with Concept Drift Detection and Prototype-based Embeddings for Graph Stream Classification
by Kleanthis Malialis, Jin Li, Christos G. Panayiotou, Marios M. Polycarpou
First submitted to arxiv on: 3 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 The proposed paper introduces a novel method for graph stream classification that can adapt to changing data distributions over time. The approach leverages incremental learning to continually update the model and selects representative graphs (prototypes) for each class, as well as creates graph embeddings. Additionally, it incorporates a loss-based concept drift detection mechanism to recalculate graph prototypes when drift is detected. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The new method helps learn from constantly evolving graph streams, which is crucial for understanding complex systems like critical infrastructure and social networks. This allows for informed decision-making in nonstationary environments where data distributions change over time. The paper’s contributions include a novel approach to graph stream classification that can handle concept drift and a loss-based detection mechanism. |
Keywords
* Artificial intelligence * Classification
