Summary of Topogcl: Topological Graph Contrastive Learning, by Yuzhou Chen and Jose Frias and Yulia R. Gel
TopoGCL: Topological Graph Contrastive Learning
by Yuzhou Chen, Jose Frias, Yulia R. Gel
First submitted to arxiv on: 25 Jun 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 proposed Topological Graph Contrastive Learning (TopoGCL) model introduces concepts of topological invariance and extended persistence on graphs to address limitations in existing graph contrastive learning approaches. By targeting topological representations of augmented views from the same graph, TopoGCL extracts latent shape properties at multiple resolutions. The model includes an extended topological layer and derived theoretical stability guarantees for its new extended persistence summary, namely, extended persistence landscapes (EPL). Experimental results on biological, chemical, and social interaction graphs demonstrate significant performance gains in unsupervised graph classification for 11 out of 12 considered datasets, with robustness under noisy scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The TopoGCL model helps computers learn better from big data. It’s like a puzzle where we find patterns in how things are connected. This is important because many things in the world are connected in complex ways, like friends on social media or atoms in molecules. The new model does this by looking at shapes and properties of these connections at different scales. This helps it learn more effectively from data without needing labels. The results show that TopoGCL works well on a variety of datasets and can handle noisy or incorrect information. |
Keywords
* Artificial intelligence * Classification * Unsupervised
