Summary of Todyformer: Towards Holistic Dynamic Graph Transformers with Structure-aware Tokenization, by Mahdi Biparva et al.
Todyformer: Towards Holistic Dynamic Graph Transformers with Structure-Aware Tokenization
by Mahdi Biparva, Raika Karimi, Faezeh Faez, Yingxue Zhang
First submitted to arxiv on: 2 Feb 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 This paper proposes a novel Transformer-based neural network called Todyformer, designed specifically for processing dynamic graphs that exhibit temporal patterns. The authors identify limitations in existing Temporal Graph Neural Networks (TGNNs), such as over-squashing and over-smoothing, which can impede their performance. To address these issues, they introduce a four-component architecture: patchifying for dynamic graphs to mitigate over-squashing, structure-aware parametric tokenization leveraging Message-Passing Neural Networks (MPNNs), a Transformer with temporal positional-encoding to capture long-range dependencies, and an encoding architecture that alternates between local and global contextualization. Experimental results on public benchmark datasets demonstrate the superiority of Todyformer compared to state-of-the-art methods for downstream tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes a new kind of computer network called Todyformer to help computers understand changing patterns in networks. Right now, special kinds of networks called Temporal Graph Neural Networks can do this, but they have some problems. They get too good at understanding what’s close and forget about things that are far away. The people who made Todyformer wanted to fix this by combining two other ideas: one that helps computers understand local patterns and another that is really good at understanding long-distance connections. They tested it on big datasets and found out that it works way better than other ways of doing the same thing. |
Keywords
* Artificial intelligence * Neural network * Positional encoding * Tokenization * Transformer
