Summary of Hierarchical Graph Pooling Based on Minimum Description Length, by Jan Von Pichowski et al.
Hierarchical Graph Pooling Based on Minimum Description Length
by Jan von Pichowski, Christopher Blöcker, Ingo Scholtes
First submitted to arxiv on: 16 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Social and Information Networks (cs.SI)
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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 MapEqPool, a novel graph pooling operator, is designed to capture the hierarchical structure of real-world graphs. By leveraging the map equation, an information-theoretic objective function, MapEqPool balances model complexity and fit, naturally implementing Occam’s razor. Empirically, MapEqPool shows competitive performance across standard graph classification datasets, outperforming various baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MapEqPool is a new way to help computers understand complex networks like social media or traffic patterns. It works by looking at the structure of these networks and finding the most important parts to keep. This helps machines learn more efficiently and make better predictions. In tests, MapEqPool did as well or better than other methods for classifying graphs. |
Keywords
» Artificial intelligence » Classification » Objective function
