Summary of Enhancing the Expressivity Of Temporal Graph Networks Through Source-target Identification, by Benedict Aaron Tjandra et al.
Enhancing the Expressivity of Temporal Graph Networks through Source-Target Identification
by Benedict Aaron Tjandra, Federico Barbero, Michael Bronstein
First submitted to arxiv on: 6 Nov 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 The paper presents a challenge to Temporal Graph Networks (TGNs), which excel in tasks like dynamic node classification and link prediction but struggle with predicting future node interactions. Despite their limitations, TGNs are outperformed by simple heuristics like persistent forecasts and moving averages over ground-truth labels. The authors propose enhancing TGNs by adding source-target identification to each interaction event message, creating a new model called TGNv2 that surpasses TGN and current temporal graph models on dynamic node affinity prediction tasks across multiple datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary TGNs are a type of machine learning model that can predict how two nodes will interact in the future. However, they don’t do very well at this task compared to simpler methods. The researchers found that using moving averages or persistent forecasts over real data actually performs better than TGNs! They also developed a new way to improve TGNs by adding more information about each interaction event message. This new model, called TGNv2, is really good at predicting future node interactions and outperforms other models on this task. |
Keywords
* Artificial intelligence * Classification * Machine learning
