Summary of A Survey on Self-supervised Graph Foundation Models: Knowledge-based Perspective, by Ziwen Zhao et al.
A Survey on Self-Supervised Graph Foundation Models: Knowledge-Based Perspective
by Ziwen Zhao, Yixin Su, Yuhua Li, Yixiong Zou, Ruixuan Li, Rui Zhang
First submitted to arxiv on: 24 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Social and Information Networks (cs.SI)
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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 presents a comprehensive survey of self-supervised learning methods for Graph Foundation Models (GFMs), focusing on the knowledge patterns embedded in graph data. The authors highlight the limitations of existing surveys, which lack comprehensiveness and take a narrow architecture-based perspective. To address this, they propose a novel knowledge-based taxonomy that categorizes self-supervised graph models by the specific graph knowledge utilized. This taxonomy covers nine categories of microscopic, mesoscopic, and macroscopic knowledge, as well as over 25 pretext tasks for pre-training GFMs. The authors also discuss various downstream task generalization strategies. By re-examining graph models based on new architectures, such as graph language models, this survey aims to provide a clearer understanding of how to construct GFMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about learning from graphs without needing labels. Graphs are like maps that show connections between things. The authors look at many ways to learn from these graphs and find patterns in them. They say current surveys of this topic have problems, so they made a new way to organize what we know about graph learning. This helps us understand how to make better models for understanding graphs. It’s like making a map that shows the connections between different ideas. |
Keywords
* Artificial intelligence * Generalization * Self supervised
