Summary of Temporal Graph Odes For Irregularly-sampled Time Series, by Alessio Gravina et al.
Temporal Graph ODEs for Irregularly-Sampled Time Series
by Alessio Gravina, Daniele Zambon, Davide Bacciu, Cesare Alippi
First submitted to arxiv on: 30 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 A novel machine learning framework for graph representation learning is presented, which overcomes the limitations of existing approaches by dealing with continuous dynamics and sporadic observations. The Temporal Graph Ordinary Differential Equation (TG-ODE) approach learns both temporal and spatial dynamics from graph streams where intervals between observations are not regularly spaced. Experimental results on several graph benchmarks demonstrate state-of-the-art performance in irregular graph stream tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Graph representation learning is important for understanding complex systems like social networks and physical systems, but current approaches make unrealistic assumptions about the data. The new Temporal Graph Ordinary Differential Equation (TG-ODE) framework addresses this issue by learning from graph streams with irregularly spaced observations. This means it can handle real-world situations where data isn’t collected at regular intervals. |
Keywords
» Artificial intelligence » Machine learning » Representation learning
