Summary of Deep-graph-sprints: Accelerated Representation Learning in Continuous-time Dynamic Graphs, by Ahmad Naser Eddin et al.
Deep-Graph-Sprints: Accelerated Representation Learning in Continuous-Time Dynamic Graphs
by Ahmad Naser Eddin, Jacopo Bono, David Aparício, Hugo Ferreira, Pedro Ribeiro, Pedro Bizarro
First submitted to arxiv on: 10 Jul 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 The paper introduces Deep-Graph-Sprints (DGS), a novel deep learning architecture designed for efficient representation learning on Continuous-time dynamic graphs (CTDGs) with low-latency inference requirements. DGS aims to address the limitations of traditional methods, including feature engineering and deep learning, which rely on manual feature crafting or suffer from high inference latency. The paper benchmarks DGS against state-of-the-art (SOTA) feature engineering and graph neural network methods using five diverse datasets. Results show that DGS achieves competitive performance while inference speed improves between 4x and 12x compared to other deep learning approaches on the benchmark datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a new way to learn from graphs that change over time, called Continuous-time dynamic graphs (CTDGs). This method is called Deep-Graph-Sprints (DGS) and it’s designed to be fast and good at finding patterns in these changing graphs. The old ways of doing this were either slow or required a lot of work to prepare the data. DGS tries to fix that by being both fast and good at learning from CTDGs. |
Keywords
» Artificial intelligence » Deep learning » Feature engineering » Graph neural network » Inference » Representation learning
