Summary of Graphgpt: Generative Pre-trained Graph Eulerian Transformer, by Qifang Zhao et al.
GraphGPT: Generative Pre-trained Graph Eulerian Transformer
by Qifang Zhao, Weidong Ren, Tianyu Li, Hong Liu, Xingsheng He, Xiaoxiao Xu
First submitted to arxiv on: 31 Dec 2023
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 introduces GraphGPT, a self-supervised generative pre-training model for graph learning. The novel architecture, called the Graph Eulerian Transformer (GET), combines standard transformer encoder or decoder architectures with an innovative graph-to-sequence transformation method. This approach converts graphs into sequences of tokens representing nodes, edges, and attributes in a reversible manner using Eulerian paths. The model is trained using self-supervised tasks such as next-token prediction and scheduled masked-token prediction, and then fine-tuned for downstream tasks like graph-, edge-, and node-level prediction. GraphGPT achieves performance comparable to or surpassing state-of-the-art methods on multiple OGB datasets, including PCQM4Mv2 and ogbl-ppa. The model’s scalability is also impressive, allowing it to scale to 2 billion parameters while maintaining performance gains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way for computers to learn about graphs, which are like maps of connections between things. The new model, called GraphGPT, takes in these graph maps and turns them into words that can be understood by computers. This helps the computer learn more about the graphs and make better predictions about what might happen next. The paper tests this model on some big datasets and shows that it works really well. It even allows the model to get very good at making predictions without needing a lot of training data, which is important for working with lots of different types of graphs. |
Keywords
* Artificial intelligence * Decoder * Encoder * Self supervised * Token * Transformer
