Summary of Retrofitting Temporal Graph Neural Networks with Transformer, by Qiang Huang et al.
Retrofitting Temporal Graph Neural Networks with Transformer
by Qiang Huang, Xiao Yan, Xin Wang, Susie Xi Rao, Zhichao Han, Fangcheng Fu, Wentao Zhang, Jiawei Jiang
First submitted to arxiv on: 9 Sep 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 approach to Temporal Graph Neural Networks (TGNNs) by incorporating Transformer’s codebase for efficient training. The authors recognize that TGNNs outperform regular GNNs by incorporating time information into graph-based operations, but require tailored training frameworks. They introduce TF-TGN, which uses the Transformer decoder as the backbone model for TGNN, allowing for the use of high-performance kernels and distributed training schemes developed for language modeling. The authors also design algorithmic components such as suffix infilling, temporal graph attention with self-loop, and causal masking self-attention to make TF-TGN work. To accelerate training, they propose methods to parallelize CSR format conversion and graph sampling. Experimental results show that TF-TGN can accelerate training by over 2.20 while providing comparable or even superior accuracy to existing state-of-the-art TGNNs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes temporal graph neural networks (TGNNs) faster and better. Researchers have been trying to use TGNNs for tasks like predicting what will happen next in a sequence of events, but it’s been slow and hard to train. The authors of this paper came up with a new way to train TGNNs that uses techniques developed for language models. This makes the training process much faster and more efficient. They also designed some special features to help the model learn better. The results show that their new approach is not only faster but also just as good or even better than other state-of-the-art methods. |
Keywords
» Artificial intelligence » Attention » Decoder » Self attention » Transformer
