Summary of A Pure Transformer Pretraining Framework on Text-attributed Graphs, by Yu Song et al.
A Pure Transformer Pretraining Framework on Text-attributed Graphs
by Yu Song, Haitao Mao, Jiachen Xiao, Jingzhe Liu, Zhikai Chen, Wei Jin, Carl Yang, Jiliang Tang, Hui Liu
First submitted to arxiv on: 19 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 proposed Graph Sequence Pretraining with Transformer (GSPT) framework treats graph structure as a prior and leverages rich feature spaces to learn refined interaction patterns that generalize across graphs. This approach samples node contexts through random walks and employs masked feature reconstruction to capture pairwise proximity in the Large Language Model (LLM)-unified feature space using a standard Transformer. By utilizing unified text representations rather than varying structures, GSPT achieves significantly better transferability among graphs within the same domain. The framework demonstrates promising empirical success on various datasets for both node classification and link prediction tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GSPT is a new way to learn about connections between things in graphs. Imagine you have a map of cities and roads, or a list of people and their relationships. This method helps computers understand how these things are connected by treating the structure of the graph as important information. It does this by looking at the features of each node (like what kind of city it is) and trying to predict what’s missing from the graph. The result is that computers can learn about connections in different graphs more easily, which could be useful for things like recommending friends or finding the shortest route. |
Keywords
» Artificial intelligence » Classification » Large language model » Pretraining » Transferability » Transformer
