Summary of Towards Graph Foundation Models: the Perspective Of Zero-shot Reasoning on Knowledge Graphs, by Kai Wang et al.
Towards Graph Foundation Models: The Perspective of Zero-shot Reasoning on Knowledge Graphs
by Kai Wang, Siqiang Luo
First submitted to arxiv on: 16 Oct 2024
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 explores the development of Graph Foundation Models that can generalize well across various graph tasks and domains without requiring extensive training or fine-tuning. The authors focus on using Knowledge Graphs (KGs) as a unified topological structure to tackle diverse tasks, addressing semantic isolation challenges in KG reasoning to effectively integrate diverse semantic and structural features. They introduce SCORE, a unified graph reasoning framework that generalizes diverse graph tasks using zero-shot learning, with semantic conditional message passing at its core. The authors evaluate the zero-shot reasoning capability of SCORE using 38 diverse graph datasets, covering node-level, link-level, and graph-level tasks across multiple domains. The results show substantial performance improvements over prior foundation models and supervised baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to make artificial intelligence better by creating a new kind of model that can work well on different types of graphs without needing a lot of training or practice. Graphs are like maps, but instead of showing places, they show relationships between things. The authors want to use these graphs as a way to solve different problems, like understanding how people connect with each other online. They developed a new method called SCORE that can do this, and tested it on 38 different datasets. The results showed that their method is much better than what’s already out there. |
Keywords
» Artificial intelligence » Fine tuning » Supervised » Zero shot
