Summary of A Primer on Temporal Graph Learning, by Aniq Ur Rahman et al.
A Primer on Temporal Graph Learning
by Aniq Ur Rahman, Justin P. Coon
First submitted to arxiv on: 8 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Discrete Mathematics (cs.DM); Social and Information Networks (cs.SI); Signal Processing (eess.SP)
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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 concept-first approach is introduced to facilitate understanding of Temporal Graph Learning (TGL) frameworks. The paper systematically presents essential concepts, including mathematical formulations, to enhance clarity. It covers various architectures such as recurrent and convolutional neural networks, transformers, and graph neural networks, which are crucial for learning in temporal and spatial domains. Additionally, classical time series forecasting methods are discussed to inspire interpretable learning solutions for TGL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how Temporal Graph Learning (TGL) works. It explains important ideas and uses math when it’s helpful. We learn about different kinds of networks that help with learning over time and in space. The paper also shows us how old methods for predicting the future can be used to make TGL more understandable. |
Keywords
* Artificial intelligence * Time series
