Summary of Control-based Graph Embeddings with Data Augmentation For Contrastive Learning, by Obaid Ullah Ahmad et al.
Control-based Graph Embeddings with Data Augmentation for Contrastive Learning
by Obaid Ullah Ahmad, Anwar Said, Mudassir Shabbir, Waseem Abbas, Xenofon Koutsoukos
First submitted to arxiv on: 7 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Multiagent Systems (cs.MA); Systems and Control (eess.SY)
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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 paper presents a novel framework for unsupervised graph representation learning based on dynamical networks defined on graphs. It introduces a contrastive learning technique that harnesses the control properties of these networks to create augmented graphs that retain structural characteristics. The approach generates new graphs by perturbing the original ones while preserving controllability properties, which enhances the effectiveness of contrastive learning frameworks for classification tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows how to learn graph representations without labeled data. It uses special networks called dynamical networks to create new versions of a graph that keep its important features. The approach is based on controlling these networks to create new graphs that are similar but not identical to the original one. This helps machines learn better from unsupervised data, which can be useful for many applications. |
Keywords
* Artificial intelligence * Classification * Representation learning * Unsupervised
